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āļāļąāļāļĐāļ°:
Data Warehousing, Risk Management, Data Analysis, TensorFlow, Big Data
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, develop and deploy advanced machine learning and artificial intelligence models focused on financial risk analytics, credit risk assessment, market risk analysis and fraud detection.
- Lead the end-to-end data science lifecycle, from problem definition and data exploration through model validation, implementation and ongoing performance monitoring.
- Mentor and supervise junior data scientists and analytical staff, fostering a culture of excellence, continuous learning and collaborative problem-solving within the te ...
- Conduct comprehensive exploratory data analysis on large-scale financial datasets to identify patterns, anomalies and opportunities for predictive modelling.
- Develop robust statistical models and econometric frameworks to quantify and forecast financial risks across various asset classes and business segments.
- Create clear and compelling data visualisations and reports that translate complex analytical findings into actionable business recommendations for senior management and risk committees.
- Collaborate closely with risk management, compliance, trading and operations teams to understand business requirements and ensure analytical solutions align with strategic objectives.
- Establish and maintain rigorous model governance frameworks, including validation protocols, backtesting procedures and documentation standards to ensure regulatory compliance.
- Stay abreast of emerging trends, methodologies and technologies in financial AI, machine learning and risk analytics, and evaluate their applicability to our business.
- Optimise data pipelines and analytical infrastructure to enhance processing efficiency, scalability and data quality across analytical systems.
- What we're looking for.
- Master's degree or higher in Data Science, Machine Learning, Statistics, Mathematics, Physics, Computer Science or a related quantitative discipline.
- Minimum 7 - 10 years of professional experience in data science, machine learning engineering or advanced analytics, with at least 4 - 5 years specifically focused on financial services, risk analytics or quantitative finance.
- Demonstrated expertise in building and deploying machine learning models in production environments, with proficiency in supervised learning, unsupervised learning, ensemble methods and deep learning techniques.
- Strong proficiency in programming languages such as Python, R or Scala, with experience in relevant libraries and frameworks (e.g. scikit-learn, TensorFlow, PyTorch, XGBoost).
- Solid understanding of financial concepts including credit risk, market risk, operational risk, counterparty risk, regulatory capital requirements and risk measurement methodologies.
- Experience with big data technologies and distributed computing frameworks such as Apache Spark, Hadoop or cloud-based analytics platforms (AWS, GCP, Azure).
- Proven track record of leading analytical projects, mentoring junior staff and driving cross-functional collaboration in a complex organisational environment.
- Strong statistical knowledge including hypothesis testing, causal inference, time series analysis and experimental design.
- Experience with financial data sources, APIs and market data platforms; familiarity with data warehousing and ETL processes is advantageous.
- Excellent communication skills with the ability to present technical concepts clearly to both technical and non-technical audiences.
- Knowledge of financial regulations such as Basel III, IFRS 9, Dodd-Frank or equivalent regional frameworks is highly desirable.
- Certification in machine learning, data science or related disciplines is a plus.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Software Architecture, Architecture, Kubernetes, TensorFlow, Leadership Skill
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, develop, and optimize AI / Machine Learning models for business applications.
- Large Language Models (LLMs).
- AI Assistants.
- Chatbots.
- Retrieval-Augmented Generation (RAG) systems.
- Develop predictive models and advanced analytics solutions.
- Design AI workflows, pipelines, and model architectures for enterprise-scale deployment.
- Improve model performance, accuracy, scalability, and reliability.
- Engineering & Infrastructure.
- Develop AI APIs, backend services, and AI-enabled platforms.
- Deploy and manage AI models on cloud infrastructure (AWS, GCP, Azure).
- CI/CD pipelines.
- Model monitoring.
- Version control.
- Governance and compliance.
- Optimize system performance, security, latency, and scalability.
- Integrate AI systems with internal enterprise systems and third-party platforms.
- Collaboration & Leadership.
- Collaborate with Product, Business, Data, and Technology teams to identify AI opportunities.
- Provide technical leadership and recommendations on AI architecture and best practices.
- Research emerging AI technologies and evaluate business applications.
- Mentor junior engineers and support capability development within the team.
- Prepare technical documentation, standards, and operational procedures.
- Bachelor's degree or higher in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, or related fields.
- Minimum 5 years of experience in AI / Machine Learning Engineering.
- Minimum 2 years of experience in Generative AI or Large Language Models (LLMs).
- Proven experience deploying AI systems into production environments.
- Technical Skills.
- Strong proficiency in Python.
- PyTorch.
- TensorFlow.
- Scikit-learn.
- LangChain / LlamaIndex.
- Deep Learning.
- Natural Language Processing (NLP).
- Vector Databases.
- Embedding Models.
- RAG Architecture.
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Docker.
- Kubernetes.
- Git.
- CI/CD pipelines.
- Knowledge of enterprise system design and software architecture.
- Preferred Qualifications.
- Experience in AI Governance, AI Security, or Responsible AI.
- Experience developing AI Agents or Autonomous Systems.
- Experience building enterprise AI platforms or AI SaaS products.
- Previous experience as a Technical Lead or Team Lead.
- Contributions to open-source projects, research publications, or innovation initiatives.
- Soft Skills.
- Strong analytical and problem-solving skills.
- Excellent communication and stakeholder management abilities.
- Leadership mindset with strong ownership and accountability.
- Ability to work effectively in cross-functional teams.
- Passion for continuous learning and emerging technologies.
- Provident Fund.
- Performance bonus.
- Life insurance.
- Medical insurance.
- Dental insurance.
- etc.
- To apply online please click the 'Apply' button below.
- https://jobs.empeo.com/tkcservices.
- For a confidential discussion about this role please contact.
- Turnkey Communication Services Public Company Limited.
- 44/44 Vibhavadi-Rangsit 60 Yake 18-1-2, Talad Bangkhen, Laksi, Bangkok 10210.
- https://www.tkc-services.com.
- https://www.facebook.com/TurnkeyCommunicationServices.
āļāļąāļāļĐāļ°:
Google Cloud Platform, Microsoft Azure, Architecture, Web Services, TensorFlow, Python, Docker
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ18,150 - āļŋ21,180, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļ§āļīāļāļąāļĒ āļāļąāļāļāļē āđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāļĢāļ°āļāļāđāļāļāđāļāđāļĨāļĒāļĩāļāļąāļāļāļēāļāļĢāļ°āļāļīāļĐāļāđ (Artificial Intelligence: AI) āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāđāļĨāļ°āđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļģāļāļēāļāļ āļēāļĒāđāļāļāļāļāđāļāļĢāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ.
- āļāļāļāđāļāļāđāļĨāļ°āļāļąāļāļāļēāđāļĄāđāļāļĨ AI āđāļāđāļ Machine Learning, Natural Language Processing (NLP), Computer Vision, Speech to Text, Speech Recognition āļĢāļ§āļĄāļāļķāļ AI Agent āđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāđāļāđāļēāļŦāļĄāļēāļĒāđāļāļīāļāļāļļāļĢāļāļīāļāļāļāļāļāļāļāđāļāļĢ.
- āļĻāļķāļāļĐāļē āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āļāļāļŠāļāļāđāļāļāđāļāđāļĨāļĒāļĩ AI āđāļŦāļĄāđ āđ āđāļāļ·āđāļāļāļģāļĄāļēāļāļĢāļ°āļĒāļļāļāļāđāđāļāđāđāļāļāļēāļĢāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāļāļāļāļāļēāļāļēāļĢ.
- āļĻāļķāļāļĐāļē āļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āļĢāļ§āļāļĢāļ§āļĄāļāļ§āļēāļĄāļāđāļāļāļāļēāļĢāļāļāļāļŦāļāđāļ§āļĒāļāļēāļāļāļđāđāđāļāđ (User Requirement) āđāļāļ·āđāļāļāļģāļĄāļēāļāļĢāļ°āļāļāļāļāļēāļĢāļāļģāļŦāļāļāđāļāļ§āļāļēāļāļŦāļĢāļ·āļāļĢāļđāļāđāļāļāļāļēāļĢāļāļąāļāļāļēāđāļāļĢāļāļāļēāļĢāļāđāļēāļ AI.
- āļŠāļāļąāļāļŠāļāļļāļāļāļēāļāļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļāļīāļāļāļļāļĢāļāļīāļ (Business Analysis) āđāļāļĒāđāļāļĨāļāļāļ§āļēāļĄāļāđāļāļāļāļēāļĢāļŦāļĢāļ·āļāļāļąāļāļŦāļēāļāļēāļāļāļļāļĢāļāļīāļāđāļŦāđāđāļāđāļāđāļāļĨāļđāļāļąāļāļāļēāļāđāļāļāļāļīāļ āđāļĨāļ°āđāļāļ·āđāļāļĄāđāļĒāļāđāļāđāļēāļŦāļĄāļēāļĒāđāļāļīāļāļāđāļĒāļāļēāļĒāđāļĨāļ°āļāļāļāđāļāļĢāđāļāđāļēāļāļąāļāđāļāļ§āļāļēāļāđāļāļāđāļāđāļĨāļĒāļĩ.
- āļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāđāļēāļ Data, IT āđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļ āļēāļĒāđāļāļāļ·āđāļ āđ āđāļāļ·āđāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āđāļāđāđāļāļāļąāļāļŦāļēāļāđāļēāļāđāļāļāđāļāđāļĨāļĒāļĩāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļĢāļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļąāđāļāļ āļēāļĒāđāļāđāļĨāļ°āļ āļēāļĒāļāļāļāļāļāļāđāļāļĢ āđāļāļ·āđāļāđāļŦāđāļāļēāļĢāļāļģāđāļāļīāļāđāļāļĢāļāļāļēāļĢāđāļāđāļāđāļāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ āđāļĨāļ°āļāļĢāļĢāļĨāļļāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāļāļēāļĄāļāļĩāđāļāļģāļŦāļāļ.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāđāļāđāļāļēāļ LLM Orchestration Framework āđāļāđāļ LangChain āļŦāļĢāļ·āļ LangGraph āđāļāļāļēāļĢāļāļąāļāļāļēāđāļāļāļāļĨāļīāđāļāļāļąāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāđāļāđāļāļēāļāđāļāļāđāļāđāļĨāļĒāļĩāļāļĨāļēāļ§āļāđāđāļāļĨāļāļāļāļĢāđāļĄ (Cloud Platform) āđāļāđāļ Microsoft Azure, Google Cloud Platform (GCP) āļŦāļĢāļ·āļ Amazon Web Services (AWS).
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāđāļāđāļēāļĢāđāļ§āļĄāļāļēāļĢāđāļāđāļāļāļąāļāļāđāļēāļāļ§āļīāļāļĒāļēāļĻāļēāļŠāļāļĢāđāļāđāļāļĄāļđāļĨ (āđāļāđāļ Kaggle Competition) āļŦāļĢāļ·āļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļąāļāļāļēāļĢāļāļąāļāļāļļāļāļāđāļāļĄāļđāļĨ (Dataset) āļāļĢāļīāļāđāļāļ āļēāļāļāļļāļĢāļāļīāļ.
- āđāļāđāļāļāļļāļāļāļĨāļāļĩāđāļĄāļĩāļāļļāļāļŠāļĄāļāļąāļāļīāđāļĨāļ°āđāļĄāđāļĄāļĩāļĨāļąāļāļĐāļāļ°āļāđāļāļāļŦāđāļēāļĄāļāļāļāļāļāļąāļāļāļēāļāļāļēāļĄāļāļāļŦāļĄāļēāļĒ āļ§āđāļēāļāđāļ§āļĒāļāļļāļāļŠāļĄāļāļąāļāļīāļĄāļēāļāļĢāļāļēāļāļŠāļģāļŦāļĢāļąāļāļāļĢāļĢāļĄāļāļēāļĢāđāļĨāļ°āļāļāļąāļāļāļēāļāļāļāļāļĢāļąāļāļ§āļīāļŠāļēāļŦāļāļīāļ āđāļĨāļ°āļĢāļ°āđāļāļĩāļĒāļāļāļāļēāļāļēāļĢāļāļāļĄāļŠāļīāļ āļ§āđāļēāļāđāļ§āļĒāļāļēāļĢāđāļāđāļāļāļąāđāļāđāļĨāļ°āļāļēāļĢāļāđāļāļāļēāļāļāļģāđāļŦāļāđāļāļāļāļąāļāļāļēāļāļāļāļēāļāļēāļĢāļāļāļĄāļŠāļīāļ.
- āļāļēāļĒāļļāđāļĄāđāđāļāļīāļ 30 āļāļĩ āļāļąāļāļāļķāļāļ§āļąāļāļāļīāļāđāļāđāļāļāļ§āļēāļĄāļāļĢāļ°āļŠāļāļāđ.
- āđāļāļĻāļāļēāļĒāļāđāļāļāļāđāļāļ āļēāļĢāļ°āļāļēāļāļāļŦāļēāļĢ āļŦāļĢāļ·āļāļāđāļēāļāļāļēāļĢāđāļāļāļāđāļāļŦāļēāļĢ (āđāļāļ āļŠāļ.43) āļŦāļĢāļ·āļāđāļāđāļŠāļģāđāļĢāđāļāļāļēāļĢāļāļķāļāļ§āļīāļāļēāļāļŦāļēāļĢāļāļąāđāļāļāļĩāļāļĩāđ 3 āļāļķāđāļāđāļ (āđāļāļ āļŠāļ.8) āļŦāļĢāļ·āļāļāļēāļĄāļāļĩāđāļāļāļēāļāļēāļĢāđāļŦāđāļāļāļ§āļĢ.
- āļāđāļāļāđāļĄāđāđāļāđāļāļāļļāļāļāļĨāļāļĩāđāļāļđāļāļāļģāļŦāļāļāļāļēāļĄāļĄāļēāļāļĢāļē 4 āđāļŦāđāļāļāļĢāļ°āļĢāļēāļāļāļąāļāļāļąāļāļīāļāđāļāļāļāļąāļāđāļĨāļ°āļāļĢāļēāļāļāļĢāļēāļĄāļāļēāļĢāļŠāļāļąāļāļŠāļāļļāļāļāļēāļāļāļēāļĢāđāļāļīāļāđāļāđāļāļēāļĢāļāđāļāļāļēāļĢāļĢāđāļēāļĒāđāļĨāļ°āļāļēāļĢāđāļāļĢāđāļāļĒāļēāļĒāļāļēāļ§āļļāļāļāļĩāđāļĄāļĩāļāļēāļāļļāļ āļēāļāļāļģāļĨāļēāļĒāļĨāđāļēāļāļŠāļđāļ āļ.āļĻ. 2559.
- āļŠāļģāđāļĢāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļāļąāđāļāđāļāđāļĢāļ°āļāļąāļāļāļĢāļīāļāļāļēāļāļĢāļĩāļāļķāđāļāđāļ āļŠāļēāļāļēāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ āļāļąāļāļāļēāļāļĢāļ°āļāļīāļĐāļāđ āļ§āļīāļāļĒāļēāļĻāļēāļŠāļāļĢāđāļāđāļāļĄāļđāļĨ āļŦāļĢāļ·āļāļŠāļēāļāļēāļ§āļīāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļĨāļ°āđāļāđāļāļāļĢāļ°āđāļĒāļāļāđāļāļąāļāļāļāļēāļāļēāļĢ āļāļēāļĄāļāļĩāđāļāļāļēāļāļēāļĢāđāļŦāđāļāļāļ§āļĢ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāļĩāđāļĒāļ§āļāļēāļāđāļāļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄāļāđāļ§āļĒāļ āļēāļĐāļē Python āđāļāđāļāļāļĒāđāļēāļāļāļĩ āđāļāđāļēāđāļāđāļāļĢāļāļŠāļĢāđāļēāļāđāļāđāļāđāļĨāļ°āļŦāļĨāļąāļāļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄāđāļāļīāļāļ§āļąāļāļāļļ (Object-Oriented Programming: OOP).
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāļŦāļĨāļąāļāļāļēāļĢāļāļāļ Machine Learning āđāļĨāļ° Deep Learning āđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļŠāļĢāđāļēāļāđāļĨāļ°āļāļāļŠāļāļ (Train) āđāļĄāđāļāļĨ AI āļāđāļ§āļĒ Framework āļĄāļēāļāļĢāļāļēāļāđāļāđāļ PyTorch āļŦāļĢāļ·āļ TensorFlow āđāļāđāļāđāļ§āļĒāļāļāđāļāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļ§āļēāļĄāđāļāļĩāđāļĒāļ§āļāļēāļāļāđāļēāļ Data Preprocessing āđāļĨāļ° Feature Engineering āļĢāļ§āļĄāļāļķāļāļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢāļāđāļāļĄāļđāļĨāļāļĩāđāđāļĄāđāļŠāļĄāļāļđāļĢāļāđāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļ§āļąāļāđāļĨāļ°āļāļĢāļ°āđāļĄāļīāļāļāļĨāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļāļāđāļĄāđāļāļĨ āļāđāļ§āļĒāļāļąāļ§āļāļĩāđāļ§āļąāļ (Metrics) āļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāļāļąāļāļĨāļąāļāļĐāļāļ°āļāļēāļ āđāļāđāļ AUC, F1-score, Precision, Recall āđāļĨāļ° RMSE.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāđāļĨāļ°āļāļąāļāļĐāļ°āļāļēāļĢāđāļāđāļāļēāļāļĢāļ°āļāļāļāļ§āļāļāļļāļĄāđāļ§āļāļĢāđāļāļąāđāļ (Version Control) āđāļāđāļ Git āđāļāļ·āđāļāđāļāđāđāļāļāļēāļĢāļāļąāļāļāļēāļĢāļāļāļĢāđāļŠāđāļāđāļāđāļĨāļ°āļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļāļĩāļĄāđāļāđāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ.
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāđāļāđāļāļēāļ Docker āđāļāļāļēāļĢāļāļąāļāļāļģ Container (Containerize) āļŠāļģāļŦāļĢāļąāļāđāļāļāļāļĨāļīāđāļāļāļąāļ āļŦāļĢāļ·āļāđāļĄāđāļāļĨāđāļāļ·āđāļāļāļāđāļāđāļāđ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāđāļēāļāļāļēāļĢāļāļīāļāļ§āļīāđāļāļĢāļēāļ°āļŦāđ āļāļēāļĢāđāļāđāđāļāļāļąāļāļŦāļēāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāļąāļāļāđāļāļ āđāļĨāļ°āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļāļĩāļĄāļāļēāļāđāļāļāļāļđāļĢāļāļēāļāļēāļĢ (Cross-functional Team) āđāļāđāļāļĩ.
- āļŦāļēāļāļĄāļĩāļāļļāļāļŠāļĄāļāļąāļāļīāđāļāļīāđāļĄāđāļāļīāļĄāļāļąāļāļāļĩāđāļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļĢāļāļĩāļāļīāđāļĻāļĐ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ§āļēāļĄāđāļāđāļēāđāļāļāđāļēāļ Generative AI āđāļĨāļ° Large Language Models (LLM) āļĢāļ§āļĄāļāļķāļāđāļāđāļēāđāļāđāļāļĢāļāļŠāļĢāđāļēāļāļŠāļāļēāļāļąāļāļĒāļāļĢāļĢāļĄ (Transformer Architecture) āđāļĨāļ°āļĄāļĩāļāļąāļāļĐāļ°āļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāļāđāļēāļ Prompt Engineering.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāđāļāđāļāļēāļ LLM Orchestration Framework āđāļāđāļ LangChain āļŦāļĢāļ·āļ LangGraph āđāļāļāļēāļĢāļāļąāļāļāļēāđāļāļāļāļĨāļīāđāļāļāļąāļ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļąāļāļāļēāđāļĨāļ°āļāļāļāđāļāļ REST API (āđāļāđāļ FastAPI) āđāļāļ·āđāļāđāļāđāđāļāļāļēāļĢāđāļāļ·āđāļāļĄāļāđāļ āļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āđāļŦāđāļāļĢāļīāļāļēāļĢāđāļĄāđāļāļĨ AI (Model Deployment) āļĢāđāļ§āļĄāļāļąāļāļĢāļ°āļāļāļāļ·āđāļ āđ āđāļāđ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāļāļēāļĢāļ°āļāļ§āļāļāļēāļĢ Continuous Integration / Continuous Deployment (CI/CD) āđāļĨāļ°āļāļąāđāļāļāļāļāļāļēāļĢāļāļģāļĢāļ°āļāļ AI āļāļķāđāļāđāļāđāļāļēāļāļāļĢāļīāļ (AI Deployment) āđāļāļ·āđāļāļāļāđāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāđāļāđāļāļēāļāđāļāļāđāļāđāļĨāļĒāļĩāļāļĨāļēāļ§āļāđāđāļāļĨāļāļāļāļĢāđāļĄ (Cloud Platform) āđāļāđāļ Microsoft Azure, Google Cloud Platform (GCP) āļŦāļĢāļ·āļ Amazon Web Services (AWS).
- āļĄāļĩāļāļĨāļāļēāļāđāļāļĢāļāļāļēāļĢāļāļąāļāļāļēāļāđāļēāļ AI āļāļĩāđāđāļāđāļāļĢāļđāļāļāļĢāļĢāļĄāđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļĢāļ§āļāļŠāļāļāđāļāđ āđāļāđāļ āļāļēāļĢāļĄāļĩ GitHub Repository āļāļĩāđāđāļŠāļāļāļāļāļĢāđāļŠāđāļāđāļāđāļĨāļ°āļāļąāļāļĐāļ°āļāļēāļĢāļŠāļĢāđāļēāļāđāļĄāđāļāļĨāļāļĢāļīāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāđāļāđāļēāļĢāđāļ§āļĄāļāļēāļĢāđāļāđāļāļāļąāļāļāđāļēāļāļ§āļīāļāļĒāļēāļĻāļēāļŠāļāļĢāđāļāđāļāļĄāļđāļĨ (āđāļāđāļ Kaggle Competition) āļŦāļĢāļ·āļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļąāļāļāļēāļĢāļāļąāļāļāļļāļāļāđāļāļĄāļđāļĨ (Dataset) āļāļĢāļīāļāđāļāļ āļēāļāļāļļāļĢāļāļīāļ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Architecture, TensorFlow, Android, Kotlin, NoSQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Build and ship Android features using Kotlin.
- Create clean mobile flows for insurance, payments, claims, renewals and financial services.
- Work with product and design to simplify complex user journeys.
- Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
- Improve app performance, crash rate, loading states, responsiveness and memory usage.
- Use analytics, user behaviour and production issues to improve the product.
- Build AI-assisted mobile experiences only where they genuinely improve the user journey.
- What We're Looking For.
- 3+ years of Android development experience using Kotlin.
- Strong Android fundamentals and experience shipping production apps.
- Good knowledge of Jetpack, Coroutines, Flow and modern Android architecture.
- Strong sense of mobile UX, usability, edge cases and user flows.
- Experience integrating REST APIs and debugging production issues.
- Fast execution, high ownership and strong attention to product quality.
- App links, GitHub, screenshots or examples of shipped work are a strong advantage.
- Tech Stack.
- Kotlin.
- Jetpack Compose.
- Android SDK.
- Coroutines & Flow.
- SQL / NoSQL.
- TensorFlow Lite (on-device inference).
- The Kind of Builder We Want.
- Thinks in user journeys, not just screens.
- Cares about making complex financial products feel simple.
- Moves fast without creating messy code.
- Notices UX, performance and reliability issues before users complain.
- Honest about what they personally built, what was team-owned and what impact they can or cannot claim.
- This Role Is Not For.
- Engineers who only want fully defined tickets.
- Developers who build screens without caring about user experience.
- People who ignore crashes, edge cases, loading states or performance.
- Engineers who move slowly in a startup environment.
- People who exaggerate impact without explaining their actual contribution.
- This role is remote, but candidates must be based in Thailand. We are hiring specifically for this market, so applicants should already be based in Thailand.
- Language.
- English is our main working language across global teams. Strong English communication is required.
- Interview Process.
- Online assessment or practical task.
- Role-specific interview.
- CEO / final round.
- For strong candidates, we aim to complete the process and make an offer within 1 week from the start of the interview process. Candidates who complete assessments quickly will be prioritized.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Architecture, TensorFlow, Android, Kotlin, NoSQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Build and ship Android features using Kotlin.
- Create clean mobile flows for insurance, payments, claims, renewals and financial services.
- Work with product and design to simplify complex user journeys.
- Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
- Improve app performance, crash rate, loading states, responsiveness and memory usage.
- Use analytics, user behaviour and production issues to improve the product.
- Build AI-assisted mobile experiences only where they genuinely improve the user journey.
- What We're Looking For.
- 3+ years of Android development experience using Kotlin.
- Strong Android fundamentals and experience shipping production apps.
- Good knowledge of Jetpack, Coroutines, Flow and modern Android architecture.
- Strong sense of mobile UX, usability, edge cases and user flows.
- Experience integrating REST APIs and debugging production issues.
- Fast execution, high ownership and strong attention to product quality.
- App links, GitHub, screenshots or examples of shipped work are a strong advantage.
- Tech Stack.
- Kotlin.
- Jetpack Compose.
- Android SDK.
- Coroutines & Flow.
- SQL / NoSQL.
- TensorFlow Lite (on-device inference).
- The Kind of Builder We Want.
- Thinks in user journeys, not just screens.
- Cares about making complex financial products feel simple.
- Moves fast without creating messy code.
- Notices UX, performance and reliability issues before users complain.
- Honest about what they personally built, what was team-owned and what impact they can or cannot claim.
- This Role Is Not For.
- Engineers who only want fully defined tickets.
- Developers who build screens without caring about user experience.
- People who ignore crashes, edge cases, loading states or performance.
- Engineers who move slowly in a startup environment.
- People who exaggerate impact without explaining their actual contribution.
- This role is remote, but candidates must be based in Thailand. We are hiring specifically for this market, so applicants should already be based in Thailand.
- Language.
- English is our main working language across global teams. Strong English communication is required.
- Interview Process.
- Online assessment or practical task.
- Role-specific interview.
- CEO / final round.
- For strong candidates, we aim to complete the process and make an offer within 1 week from the start of the interview process. Candidates who complete assessments quickly will be prioritized.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Architecture, TensorFlow, Android, Kotlin, NoSQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Our mission is to make money smart, reliable and within reach for everyone. In 2019, we built the first mobile-first, insurance platform, enabling insurance to be accessible online by millions in the region.
- Today, it's the leading insurance platform in Southeast Asia. Today, we are expanding ways to help people in Asia and it includes spending, saving, investing, exchanging, travelling, and more. Our objective is to build tools that will help people get more from their money and at ease and self reliance, become an expert. We believe AI ...
- We are looking for the most talented and driven people we can find. We are looking for people who work for their passion, not counting hours. Who loves building great next-generation products, not status quo. Who cares about redefining how everyone around us can get the best financial applications, not for an exclusive few.
- We have teams working around the world, with over 20 nationalities and growing from our offices and remotely. Join us and build a better future.
- The Role.
- We are looking for Android engineers to build the native Android experience for KIRA's AI Neobank App.
- This role is for someone who cares about clean user flows, fast performance, product quality and shipping reliable mobile features used by real customers.
- What You'll Own.
- Build and ship Android features using Kotlin.
- Create clean mobile flows for insurance, payments, claims, renewals and financial services.
- Work with product and design to simplify complex user journeys.
- Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
- Improve app performance, crash rate, loading states, responsiveness and memory usage.
- Use analytics, user behaviour and production issues to improve the product.
- Build AI-assisted mobile experiences only where they genuinely improve the user journey.
- What We're Looking For.
- 3+ years of Android development experience using Kotlin.
- Strong Android fundamentals and experience shipping production apps.
- Good knowledge of Jetpack, Coroutines, Flow and modern Android architecture.
- Strong sense of mobile UX, usability, edge cases and user flows.
- Experience integrating REST APIs and debugging production issues.
- Fast execution, high ownership and strong attention to product quality.
- App links, GitHub, screenshots or examples of shipped work are a strong advantage.
- Tech Stack.
- Kotlin.
- Jetpack Compose.
- Android SDK.
- Coroutines & Flow.
- SQL / NoSQL.
- TensorFlow Lite (on-device inference).
- The Kind of Builder We Want.
- Thinks in user journeys, not just screens.
- Cares about making complex financial products feel simple.
- Moves fast without creating messy code.
- Notices UX, performance and reliability issues before users complain.
- Honest about what they personally built, what was team-owned and what impact they can or cannot claim.
- This Role Is Not For.
- Engineers who only want fully defined tickets.
- Developers who build screens without caring about user experience.
- People who ignore crashes, edge cases, loading states or performance.
- Engineers who move slowly in a startup environment.
- People who exaggerate impact without explaining their actual contribution.
- This role is remote, but candidates must be based in.
- Thailand. We are hiring specifically for this market, so applicants should already be based in.
- Thailand.
- Language.
- English is our main working language across global teams. Strong English communication is required.
- Interview Process.
- Online assessment or practical task.
- Role-specific interview.
- CEO / final round.
- For strong candidates, we aim to complete the process and make an offer within 1 week from the start of the interview process. Candidates who complete assessments quickly will be prioritized.
āļāļąāļāļĐāļ°:
Architecture, Automation, Kubernetes, TensorFlow, Python, Docker, Java, SQL, C++
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļēāļĢāļāļąāļāļāļē AI & Machine Learning Models.
- Generative AI & Automation Integration.
- MLOps & Infrastructure Management.
- Governance, Security & Compliance.
- āļāļēāļĢāļĻāļķāļāļĐāļēāđāļĨāļ°āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ.
- āļāļĢāļīāļāļāļēāļāļĢāļĩāļŦāļĢāļ·āļāđāļ āļŠāļēāļāļēāļ§āļīāļāļĒāļēāļāļēāļĢāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ, āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ, āļ§āļīāļāļĒāļēāļāļēāļĢāļāđāļāļĄāļđāļĨ (Data Science), āļāļąāļāļāļēāļāļĢāļ°āļāļīāļĐāļāđ (AI) āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļāđāļāļŠāļēāļĒāļāļēāļ AI / Machine Learning Engineer āļŦāļĢāļ·āļ Data Scientist āļāļĒāđāļēāļāļāđāļāļĒ 2 - 5 āļāļĩ.
- āļŦāļēāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļļāļĢāļāļīāļ āļāļāļēāļāļēāļĢ, āđāļāđāļāļāļāđ, āļŠāļāļēāļāļąāļāļāļēāļĢāđāļāļīāļ āļŦāļĢāļ·āļ AMC (āļāļĢāļīāļŦāļēāļĢāļŠāļīāļāļāļĢāļąāļāļĒāđ) āļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļīāđāļĻāļĐ.
- āļāļąāļāļĐāļ°āļāļēāļāđāļāļāļāļīāļ (Technical Skills).
- Programming Languages: Python (āđāļāđāļāđāļāđāļāļŦāļĨāļąāļ), R, C++, Java āļŦāļĢāļ·āļ SQL.
- AI/ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost, OpenCV, Hugging Face.
- Generative AI & NLP: LangChain, LlamaIndex, OpenAI API, RAG Architecture, Vector Databases (ChromaDB, Pinecone, Milvus).
- MLOps & Cloud: Docker, Kubernetes, MLflow, Airflow, AWS / Azure / GCP.
- Data Processing: Pandas, NumPy, Spark.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Electrical Engineering, Production Engineering, Kubernetes, TensorFlow, Python, Docker
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Amity, through its AI Research and Application Center, is advancing the frontier of applied AI research across natural language processing, large language models, agentic systems and generative AI. We build intelligent products that serve millions of users and partner organisations across Southeast Asia and Europe. Our research engineers sit at the intersection of scientific inquiry and production engineering - discovering new methods, validating them rigorously, and shipping them into real-world products.
- As an AI Research Engineer at Amity.
- AI Research and Application Center.
- you will own ambitious research goals while ensuring that breakthroughs translate into scalable, production-grade systems.
- Identify high-impact research problems, formulate hypotheses, design experiments and advance the state of the art in areas aligned with the lab's mission.
- Publish findings at top-tier conferences and top-tier leaderboard and contribute to the broader AI research community.
- Bridge the gap between research prototypes and production systems, ensuring novel methods are robust, efficient and deployable at scale.
- Shape the lab's research roadmap and propose initiatives that create measurable business and societal value.
- Mentor junior researchers and engineers, fostering a culture of scientific rigour and collaborative innovation.
- Research & Experimentation.
- large language models., NLP, computer vision, reinforcement learning, generative models, agentic AI or multimodal learning.
- Design and run rigorous experiments - including ablation studies, benchmark evaluations and statistical analyses - to validate new methods and architectures.
- Survey, reproduce and extend state-of-the-art results from recent literature; maintain a reading group culture within the team.
- Develop novel algorithms, model architectures and training strategies that push performance boundaries on real-world tasks.
- Model Development & Optimisation.
- Design, train and fine-tune large-scale deep learning models (LLMs, diffusion models, multi-modal models) using modern frameworks such as PyTorch, TRL, Unsloth or verl. (Reinforcement Learning Experience is plus).
- Optimise model performance through techniques such as knowledge distillation, quantisation, pruning, mixed-precision training and efficient attention mechanisms.
- Build and improve training infrastructure for distributed, large-scale model training across GPU/TPU clusters.
- Develop evaluation frameworks and metrics to systematically measure model quality, safety and robustness.
- Applied Research & Productionisation.
- Translate research outcomes into production-ready features - building proof-of-concepts (PoCs), prototypes and scalable AI services.
- Design and operate RAG pipelines (ingestion, chunking, embeddings, hybrid search, re-rankers) with vector databases (pgvector, Pinecone, Weaviate, OpenSearch) to support retrieval-augmented applications.
- Architect and ship LLM-powered agents and chatbots using agentic patterns (tool/function calling, planning, memory, multi-agent orchestration) with robust safety and fallback mechanisms.
- Collaborate with product and engineering teams to integrate AI capabilities into customer-facing platforms via APIs and microservices.
- Data & Infrastructure.
- Curate, clean and build high-quality datasets for pre-training, fine-tuning and evaluation; design data pipelines for continuous data collection and annotation.
- Implement and maintain scalable ML infrastructure using Docker, Kubernetes, CI/CD and experiment-tracking tools (MLflow, Weights & Biases, or similar).
- Monitor deployed models, design automated retraining pipelines and ensure ongoing model quality through observability and alerting.
- Knowledge Sharing & Community.
- Author technical papers, internal reports and blog posts that communicate research findings to both technical and non-technical audiences.
- Present research at internal seminars, external conferences and community meetups.
- Contribute to open-source projects and public benchmarks to enhance Amity's visibility in the research community.
- Required.
- Master's or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering or a related quantitative field.
- 3+ years of hands-on experience in AI/ML research or research engineering, with demonstrated ability to design experiments, analyse results and iterate on methods.
- At least one first-author or co-author publication at a recognised venue (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, AAAI, or equivalent), or equivalent demonstrated research output (patents, technical reports, significant open-source contributions).
- Strong proficiency with Python and modern deep learning frameworks (PyTorch, JAX, or TensorFlow); solid understanding of model architectures (Transformers, diffusion models, GNNs) and training techniques (RLHF, DPO, SFT, pre-training).
- Ability to write clean, maintainable, production-quality code; familiarity with software engineering best practices (version control, code review, testing, CI/CD).
- Strong grounding in linear algebra, probability, statistics, optimisation and information theory.
- Experience designing agentic architectures (tool/function calling, planning, memory, multi-agent orchestration via frameworks such as LangChain, LlamaIndex, AutoGen or CrewAI).
- Hands-on experience with retrieval-augmented generation pipelines, embedding models, hybrid search and vector databases.
- Experience with large-scale distributed training across multi-GPU/TPU environments (DeepSpeed, FSDP, Megatron-LM or similar).
- Working knowledge of cloud platforms (AWS, GCP or Azure) and ML operations tooling (MLflow, W&B, Kubeflow).
- Active contributions to well-known AI/ML open-source projects or libraries.
- Excellent written and verbal communication skills; ability to distill complex research into clear recommendations for diverse stakeholders.
- At Amity Solutions, we are dedicated to creating a dynamic and supportive work environment that prioritizes growth, learning, and inclusivity. As an equal opportunity employer, we welcome applicants from all backgrounds, embracing diversity in ethnicity, gender, disability, religion, belief, sexual orientation, and age. Join our Bangkok team to enjoy a wide range of benefits as we innovate and grow together.
- Discover more about our team values, benefits, and career opportunities at Amity Solutions Bangkok on our.
- official website.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Analytical Thinking, Data Analysis, TensorFlow, Big Data, Python, Hadoop
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- At Gosoft (Thailand) Co., Ltd., we are looking for an experienced.
- AI and Data Scientist.
- to lead the development of advanced AI and machine learning solutions that drive innovation and business impact across our digital platforms.
- In this role, you will design and deploy large-scale machine learning systems, develop advanced analytical models, and translate complex data into strategic insights. You will collaborate with cross-functional teams including engineering, product, and business stakeholders to solve high-impact challenges in the retail and e-commerce ecosystem.
- Our team builds data-driven solutions that support platforms such as.
- 7-Eleven Thailand.
- and.
- 7-Delivery., including Search Engines, Recommender Systems, Demand Forecasting, Customer Analytics, and other AI-powered retail solutions.
- Lead the.
- design, development, and deployment of advanced machine learning and AI solutions.
- to solve complex business problems.
- Develop and implement.
- predictive models, recommendation systems, forecasting models, and optimization algorithms.
- Architect and deploy.
- end-to-end machine learning pipelines., from data ingestion and feature engineering to model deployment and monitoring.
- Analyze large-scale structured and unstructured datasets to uncover.
- actionable insights and strategic opportunities.
- Collaborate with engineering, product, and business teams to translate.
- business challenges into scalable data science solutions.
- Drive the adoption of.
- best practices in machine learning, experimentation, and model governance.
- Provide.
- technical leadership and mentorship.
- to data scientists and data engineers.
- Evaluate and integrate.
- state-of-the-art AI/ML techniques.
- including deep learning, large language models, and advanced analytics.
- Work closely with data engineering teams to ensure.
- robust data pipelines and scalable ML infrastructure.
- Communicate insights and technical findings to both.
- technical and executive stakeholders.
- Required.
- Bachelors or Masters degree in.
- Computer Science, Data Science, Statistics, Mathematics, or a related field.
- 6 - 10+ years of experience.
- in data science, machine learning, or AI-related roles.
- Strong expertise in.
- machine learning, statistical modeling, and advanced analytics.
- Proficiency in.
- Python and data science ecosystems.
- (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.).
- Experience designing.
- end-to-end machine learning pipelines and production ML systems.
- Strong experience working with.
- large-scale datasets and big data technologies.
- Ability to translate.
- complex data analysis into business insights and strategic recommendations.
- Strong problem-solving, analytical thinking, and leadership capabilities.
- Excellent communication skills and the ability to present technical concepts to.
- both technical and non-technical stakeholders.
- Preferred.
- Experience in.
- retail, e-commerce, or recommendation systems.
- Experience with.
- search ranking, recommender systems, personalization, or demand forecasting.
- Familiarity with.
- cloud platforms.
- such as AWS, Azure, or GCP.
- Experience with.
- big data technologies.
- such as Spark, Hadoop, or Databricks.
- Experience deploying models using.
- MLOps frameworks and tools.
- Knowledge of.
- deep learning, NLP, or generative AI.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
2 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Google Cloud Platform, Google Analytics, TensorFlow, Big Data, Tableau, Python, Hadoop, Kafka, Scala, Java, SQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Develop big data solutions for batch processing and near real-time streaming.
- Retrieve, prepare, and process a rich data variety of data sources.
- Work with business domain experts, data scientists and application developers to identify data that is relevant for analysis.
- Keep abreast of new developments in the big data ecosystem and learn new technologies.
- Triage code problems and data-related issues.
- 3+ years of experience with relational database systems, with expertise in SQL.
- 3+ years of programming experience in Java, Python, Scala or similar.
- 3+ years of experience in coding in data management, data warehousing or unstructured data environments.
- Experience with cloud-based platforms such as AWS, Google Cloud platform or similar.
- Experience building complex pipelines using automated workflow is a plus, e.g. Luigi, Airflow, Oozie or similar.
- Experience with parallel data processing is a plus, e.g. MapReduce, Hadoop, Spark or similar.
- Experience with streaming technologies is a plus, e.g. Kafka, AWS Kinesis or similar.
- Experience with Business Intelligence tools and platforms is a plus, e.g. Tableau, QlikView, PowerBI, Google Analytics or similar.
- Experience with Machine Learning is a plus, e.g. TensorFlow, NumPy, Scikit-Learn, Mahout or similar.
- Good communication in English.
āļāļąāļāļĐāļ°:
Big Data, Power BI, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Modeling & Machine Learning: āļāļąāļāļāļē āļāļāļŠāļāļ āđāļĨāļ°āļāļģ Machine Learning Models āđāļāđāļāđāļāļēāļāļāļĢāļīāļ (Production) āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāđāļāđāđāļāļāļąāļāļŦāļēāđāļĨāļ°āļŠāļĢāđāļēāļāđāļāļāļēāļŠāļāļēāļāļāļļāļĢāļāļīāļ āđāļāđāļ Sales Forecasting, Optimization, Fraud Detection āđāļĨāļ° Use Cases āļāļ·āđāļ āđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- Advanced Analytics: āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļĒāđāļāđāđāļāļāļāļīāļāļāļēāļāļŠāļāļīāļāļīāļāļąāđāļāļŠāļđāļāđāļĨāļ° Algorithms āđāļāļ·āđāļāļāđāļāļŦāļēāļĢāļđāļāđāļāļ (Patterns) āļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđ āđāļĨāļ°āļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļāļāļēāļāļāđāļāļĄāļđāļĨāļāļāļēāļāđāļŦāļāđ (Big Data) āļāļąāđāļāđāļāđāļāļąāđāļāļāļāļ Data Exploration āđāļāļāļāļāļķāļ Advanced Analytics.
- Dashboard & Reporting: āļāļąāļāļāļē āļāļđāđāļĨ āđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļ Dashboard āļāđāļ§āļĒ Power BI āđāļāļ·āđāļāđāļāđāļāļīāļāļāļēāļĄ ...
- Business Analysis: āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļ·āđāļāļāđāļāļŦāļē Insights āđāļāļ§āđāļāđāļĄ (Trends) āđāļāļāļēāļŠāļāļēāļāļāļļāļĢāļāļīāļ āđāļĨāļ°āļŠāļēāđāļŦāļāļļāļāļāļāļāļąāļāļŦāļē āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļļāļĢāļāļīāļāļāđāļēāļ āđ āļ āļēāļĒāđāļ BCP Group āđāļāđāļ Marketing, Refinery, Solar, Biofuel āđāļĨāļ°āļāļļāļĢāļāļīāļāļāļ·āđāļ āđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- Data Interpretation & Business Insights: āđāļāļĨāļāļĨāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļŦāđāđāļāđāļāļāđāļāđāļŠāļāļāđāļāļ°āļāļĩāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļāļāļīāļāļąāļāļīāđāļāđ (Actionable Insights) āđāļāļ·āđāļāđāļāļīāđāļĄāļĢāļēāļĒāđāļāđ āļĨāļāļāđāļāļāļļāļ āļŦāļĢāļ·āļāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāļāļāļāļļāļĢāļāļīāļ āđāļāđāļ āđāļĢāļāļāļĨāļąāđāļāļāđāļģāļĄāļąāļ āļŠāļāļēāļāļĩāļāļĢāļīāļāļēāļĢ āđāļĨāļ°āļāļļāļĢāļāļīāļāļāļ·āđāļ āđ.
- Insight Communication: āļāļąāļāļāļģ Data Visualization āđāļĨāļ°āļāļģāđāļŠāļāļāļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĩāđāļāļąāļāļāđāļāļāđāļŦāđāļāļĒāļđāđāđāļāļĢāļđāļāđāļāļāļāļĩāđāđāļāđāļēāđāļāļāđāļēāļĒ āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāļąāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļāļļāļĢāļāļīāļāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ āđāļāļĒāļŠāļēāļĄāļēāļĢāļāđāļāđ Power BI āđāļāļāļēāļĢāļāļģāđāļŠāļāļāļāđāļāļĄāļđāļĨāđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- Business Value Delivery: āļŠāļēāļĄāļēāļĢāļāđāļāļ·āđāļāļĄāđāļĒāļāļāļĨāļāļēāļāļāđāļēāļ Data & Analytics āđāļŦāđāđāļāļīāļ Business Value āļāļĩāđāļ§āļąāļāļāļĨāđāļāđ āđāļĨāļ°āļĢāļąāļāļāļīāļāļāļāļ KPI āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļļāļāļāđāļēāļāļēāļāļāļļāļĢāļāļīāļ āđāļāđāļ Revenue Enhancement, Cost Saving, Productivity Improvement āļŦāļĢāļ·āļ Efficiency Improvement.
- āļāļĢāļīāļāļāļēāļāļĢāļĩ/āđāļ āļŠāļēāļāļēāļ§āļīāļāļĒāļēāļāļēāļĢāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ, āļŠāļāļīāļāļī, āļāļāļīāļāļĻāļēāļŠāļāļĢāđ, āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļāļĢāđ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļāļāđāļēāļ Data Analytics/Business Intelligence 1-3 āļāļĩ.
- āļāļąāļāļĐāļ°āļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄ Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) āļāļĒāđāļēāļāļāļĩ.
- āđāļāļĩāđāļĒāļ§āļāļēāļāļāļēāļĢāđāļāđ SQL āļŠāļģāļŦāļĢāļąāļāļāļķāļāļāđāļāļĄāļđāļĨāđāļāļĢāļ°āļāļąāļāļŠāļđāļ.
- āļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđāđāļāļĢāļ·āđāļāļāļĄāļ·āļ Visualization (Power BI).
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāļāļĢāļīāļāļāļāļēāļāļāļļāļĢāļāļīāļ (Business Acumen) āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāđāļāļĄāļđāļĨāļāļĩāđāļāļąāļāļāđāļāļāđāļāđāļāļĩ.
- āļāļĪāļāļīāļāļĢāļĢāļĄāđāļĨāļ°āļāļļāļāļĨāļąāļāļĐāļāļ°āļāļĩāđāļāļēāļāļŦāļ§āļąāļ (Behavioral Competencies).
- Collaboration & Teamwork: āļĄāļĩāļāļąāļĻāļāļāļāļīāđāļāļāļēāļĢāļāļģāļāļēāļāđāļāđāļāļāļĩāļĄ āļāļĢāđāļāļĄāđāļŦāđāļāļ§āļēāļĄāļĢāđāļ§āļĄāļĄāļ·āļāđāļĨāļ°āļŠāļāļąāļāļŠāļāļļāļāđāļāļ·āđāļāļāļĢāđāļ§āļĄāļāļēāļāđāļāļ·āđāļāđāļŦāđāļāļĩāļĄāļŠāļēāļĄāļēāļĢāļāļāļĢāļĢāļĨāļļāđāļāđāļēāļŦāļĄāļēāļĒāļĢāđāļ§āļĄāļāļąāļ.
- Business Mindset: āļŠāļēāļĄāļēāļĢāļāļĄāļāļāļāļēāļāļāđāļēāļ Data & Analytics āđāļāļĄāļļāļĄāļĄāļāļāļāļāļāļāļļāļĢāļāļīāļ āđāļĨāļ°āļĄāļļāđāļāđāļāđāļāļāļēāļĢāļŠāļĢāđāļēāļāļāļĨāļĨāļąāļāļāđāļāļĩāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāđāļāđāļāļĢāļ°āđāļĒāļāļāđāđāļĨāļ°āļŠāļĢāđāļēāļāļāļļāļāļāđāļēāđāļŦāđāļāļąāļāļāļāļāđāļāļĢāđāļāđāļāļĢāļīāļ.
- Problem Solving: āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļąāļāļŦāļēāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ āļŠāļēāļĄāļēāļĢāļāļāđāļāļŦāļēāļŠāļēāđāļŦāļāļļāđāļĨāļ°āļāļģāļāđāļāļĄāļđāļĨāļĄāļēāđāļāđāđāļāļāļēāļĢāļāļąāļāļāļēāđāļāļ§āļāļēāļāđāļāđāđāļāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄ.
- Communication: āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāđāļāļĄāļđāļĨāđāļāļīāļāđāļāļāļāļīāļāđāļĨāļ°āļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļŦāđāļāļđāđāļāļĩāđāđāļĄāđāļĄāļĩāļāļ·āđāļāļāļēāļāļāđāļēāļ Data āđāļāđāļēāđāļāđāļāđāļāļĒāđāļēāļāļāļąāļāđāļāļ.
- Ownership & Flexibility: āļĄāļĩāļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļāļāđāļāļāļēāļ āļĄāļĩāļāļ§āļēāļĄāļĒāļ·āļāļŦāļĒāļļāđāļ āđāļĨāļ°āļāļĢāđāļāļĄāļŠāļāļąāļāļŠāļāļļāļāļāļēāļāđāļāļāđāļ§āļāđāļ§āļĨāļēāļāļĩāđāļāļģāđāļāđāļāđāļāļ·āđāļāđāļŦāđāļāļēāļāļŦāļĢāļ·āļāđāļāļĢāļāļāļēāļĢāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāđāļāļēāļĄāđāļāđāļēāļŦāļĄāļēāļĒ.
- āļĨāļąāļāļĐāļāļ°āđāļĨāļ°āđāļāļ·āđāļāļāđāļāļāļēāļĢāļāļģāļāļēāļ (Working Conditions).
- āļŠāļēāļĄāļēāļĢāļāļāļāļīāļāļąāļāļīāļāļēāļāļāļĢāļ°āļāļģāļŠāļģāļāļąāļāļāļēāļāļāļēāļĄāđāļ§āļĨāļēāļāļģāļāļēāļāļāļāļāļāļĢāļīāļĐāļąāļ 5 āļ§āļąāļāļāđāļāļŠāļąāļāļāļēāļŦāđ.
- āļŠāļēāļĄāļēāļĢāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļāļāđāļēāļ Data Preparation, Data Loading, Data Cleansing āļŦāļĢāļ·āļāļāļēāļĢāļāļģāļĢāļ°āļāļāļāļķāđāļ Production āļāļāļāđāļ§āļĨāļēāļāļģāļāļēāļāļāļāļāļīāļŦāļĢāļ·āļāđāļāļ§āļąāļāļŦāļĒāļļāļāđāļāđāđāļāđāļāļāļĢāļąāđāļāļāļĢāļēāļ§ āļāļēāļĄāļāļ§āļēāļĄāļāļģāđāļāđāļāļāļāļāđāļāļĢāļāļāļēāļĢ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Data Analysis, Enthusiastic, Architecture, Electronics, Automation
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Serve as a forward deployed AI engineer embedded with the Bangkok manufacturing team to identify high-impact AI opportunities across process engineering, yield improvement, visual inspection, quality control, operations, and reporting workflows.
- Partner with manufacturing, process, yield, quality, and operations teams to understand workflows, pain points, data sources, and decision-making needs.
- Conduct deep operational assessments at manufacturing sites by observing workflows, interviewing frontline employees, supervisors, trainers, quality leaders, and operat ...
- Design, prototype, and productionize AI/ML solutions for manufacturing use cases, including visual inspection, defect classification, anomaly detection, predictive analytics, root-cause analysis, process optimization, and yield improvement.
- Translate business and manufacturing problems into technical solution designs, including data requirements, model approach, architecture, validation strategy, user interface needs, and deployment path.
- Work with structured and unstructured manufacturing data from databases, equipment logs, inspection systems, MES, yield systems, engineering reports, and other plant data sources.
- Communicate technical concepts clearly to both technical and non-technical stakeholders, including engineers, operators, managers, and senior leaders.
- Mentor local engineers and analysts on AI tools, data science methods, model interpretation, and practical AI adoption in manufacturing workflows.
- 5+ years of experience in data science, machine learning, AI engineering, manufacturing analytics, process engineering analytics, or a closely related technical field.
- Strong hands-on experience developing and deploying AI/ML models in real-world industrial or manufacturing environments.
- Practical experience using LLMs and generative AI tools for data analysis, workflow automation, knowledge retrieval, reporting, or engineering productivity applications.
- Strong programming skills in Python and common AI/ML frameworks such as PyTorch, TensorFlow, scikit-learn, OpenCV, and LangChain/LlamaIndex or similar frameworks.
- Experience working with relational databases and SQL, preferably including Oracle or other enterprise database systems.
- Ability to build end-to-end prototypes, including data extraction, model development, backend logic, simple user interfaces, dashboards, APIs, or workflow tools.
- Strong understanding of manufacturing data, process variation, yield analysis, quality systems, equipment data, inspection data, and root-cause analysis methods.
- English communication skills good enough to interact with US and Europe teams.
- Preferred Qualifications.
- Experience in semiconductor, photonics, electronics, optical components, precision manufacturing, or other high-volume advanced manufacturing environments.
- Experience with manufacturing systems such as MES, SPC, yield management systems, equipment automation systems, inspection platforms, or quality management systems.
- We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
- We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
- Please contact us to request accommodation.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
DevOps
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Us.
- With offices in 152 countries and nearly 328,000 professionals, we are one of the world's leading professional services networks, helping organisations and individuals create lasting value through Assurance, Tax and Advisory services. For over 40 years, we have contributed to the Middle East's transformation journey, partnering with governments and businesses to deliver sustainable solutions. Today, more than 12,000 of us across Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Oman, Palestine, ...
- Line of Service Overview.
- Internal Firm Services (IFS) brings together the specialist functions that enable PwC Middle East to run, grow and transform - supporting our people, clients and business with the capabilities needed for the future of work. A career in IFS offers diverse opportunities across Human Capital, Finance, Technology & AI, Clients & Markets, Marketing & Communications, Risk & Quality, Partner Development, Office Management, and Transformation & Change. As we continue to embed data, technology and new-age AI into how we work, we are hiring talent who can help our teams become more AI-enabled, digitally confident and ready to create greater value across PwC Middle East.
- Business Unit Overview.
- T.
- he AI Centre of Excellence (AI CoE) is our central engine for AI strategy, product build, and scale. It governs standards and cost and risk controls, runs AI Factory squads to design and ship custom AI solutions and reusable frameworks (LLM and RAG stacks, integrations on Azure), provides technical consultation on architecture, solution design, and best practices, and drives workforce transformation through training and upskilling to accelerate adoption across our lines of service.
- Operating within IFS, the CoE partners with Experience Design and adjacent technology teams, follows a codified operating model and RACI for approvals and observability, and supports proposals, demos, and delivery with a focus on distinctive outcomes and trusted leadership.
- How You'll Contribute.
- As.
- Design, build, and ship AI-powered products - integrating machine learning models and large language models into production applications such as RAG pipelines, agentic systems, and AI-powered workflows.
- Develop backend services in Python for model inference, prompt orchestration, and pipeline management, exposing them through RESTful APIs and microservices.
- Participate in design discussions and architectural reviews, and uphold coding standards.
- Translate conceptual ideas and business requirements into working solutions, and evaluate new tools, frameworks, and platforms for client use cases.
- Champion the effective and responsible use of AI-assisted coding tools to improve team productivity and quality.
- Collaborate on CI/CD pipelines and DevOps practices (Docker containerization, automation) for reliable delivery.
- Communicate clearly with technical and non-technical audiences, explaining complex topics in a simple way.
- Be able to conduct technical Feasibility studies from Business Ideas.
- Stay up to date with emerging AI advancements and industry trends, and contribute to internal knowledge sharing, documentation, and reusable accelerators.
- What You'll Bring.
- Experience.
- 1-3 years of professional software or AI engineering experience in an individual-contributor role.
- Demonstrated experience building and shipping AI-powered products (RAG pipelines, agentic systems, AI-powered workflows).
- Education & Certifications.
- Bachelor's degree in Computer Science, Software Engineering, or a related technical field.
- Relevant Azure or Microsoft certifications are a plus.
- Technical Skills & Specialized Knowledge.
- Strong proficiency in Python for backend development.
- Experience integrating machine learning models or large language models into production applications.
- Familiarity with AI/ML concepts and frameworks (e.g., TensorFlow, PyTorch, scikit-learn), computer vision, and popular LLM APIs (e.g., GPT, Claude, Gemini).
- Front-end or full-stack capabilities; experience with React (Next.js) and Tailwind CSS is a plus.
- Experience with RESTful APIs and microservices architecture.
- Version control using Git; understanding of DevOps practices and Docker containerization.
- Experience with cloud platforms (AWS, Azure, or GCP).
- Attributes & Soft Skills.
- Strong problem-solving and analytical thinking.
- Innovation mindset with a passion for emerging technologies.
- Collaborative team player, able to work closely with technologists, researchers, and business stakeholders.
- Self-motivated, adaptable, and comfortable with ambiguity in fast-paced environments.
- Excellent verbal and written communication in English.
- How You'll Make a Difference.
- At PwC Midde East, we expect all our people to embody the skills and behaviours of.
- The PwC Professional.
- framework, helping us deliver on our strategy while growing and developing as leaders at every level.
- Why You'll Love Working at PwC.
- At PwC Middle East, you'll find more than just a job - you'll build a meaningful career, supported by rewards and benefits that help you thrive. We offer competitive pay, comprehensive benefits, and programs that promote well-being, balance, and personal growth. You'll have access to continuous learning, digital upskilling, and a collaborative environment that values innovation, mentorship, and diversity. Are you ready to make a difference? Want to unlock new value by applying your unique perspective and talents? You can grow exponentially here. Discover more about.
- Life at PwC Middle East.
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The Senior AI Engineer position involves designing and implementing scalable AI systems for enterprise applications, including Machine Learning, Computer Vision, Generative AI, and AI Agent platforms. Key duties encompass developing production-ready AI services with optimized performance and cost efficiency. The role requires building AI agents from single-purpose to multi-step, tool-using systems with appropriate autonomy and safeguards. Engineers will integrate ML and generative AI models through APIs and microservices, deploy AI workloads on cloud and on-premise GPU infrastructu ...
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