Job Purpose

The AI Engineer is responsible for developing, deploying, and maintaining production-grade AI/ML systems that deliver measurable business value. The role involves building modern Generative AI and Large Language Model (LLM) solutions alongside developing traditional statistical and machine learning models. Working closely with the Data team, the AI Engineer leverages the company's enterprise data platform to source, prepare, and embed high-quality data. The position emphasizes hands-on engineering, continuous learning, and a practical problem-solving mindset. It involves selecting the most appropriate methodology for each task and turning applied prototypes into dependable, scalable, and cost-efficient production services.

Major Responsibility

• Build and Optimize Modern AI Applications: Design and deploy a range of LLM-powered solutions, including Retrieval-Augmented Generation (RAG) pipelines, natural-language data querying, tool-using and function-calling workflows, and other Generative AI applications, to deliver accurate, grounded, and citable responses.

• Develop Statistical and Machine Learning Models: Develop, deploy, and maintain traditional statistical models and machine learning algorithms (e.g., regression, classification, clustering) to solve structured data problems and complement broader AI initiatives.

• Collaborate with the Data Team: Work together on data preprocessing, embedding generation, and pipeline integration, leveraging the AWS enterprise data platform to supply clean, well-governed data for AI workloads.

• Optimize and Harden Production Services: Improve production AI services for reliability, scalability, latency, and cost, document solutions clearly, and communicate outcomes to both technical and non-technical stakeholders.

• Monitor and Observe Production AI: Implement tracing, observability, and dashboards to track quality, latency, drift, and cost-per-task across the model lifecycle.

• Ensure AI Ethical Standards, Compliance, and Data Privacy: Apply input/output guardrails, PII redaction, access controls, and grounding safeguards, in alignment with internal data governance policies and emerging AI regulations.

• Mitigate AI-Assisted Development Risks: Critically evaluate and audit AI-generated code to prevent security vulnerabilities, logic flaws, and technical debt, ensuring deep technical understanding rather than over-reliance on automated code generation.

• Monitor and Observe Production AI: Implement tracing, observability, and dashboards to track quality, latency, drift, and cost-per-task across the model lifecycle.

• Ensure AI Ethical Standards, Compliance, and Data Privacy: Apply input/output guardrails, PII redaction, access controls, and grounding safeguards, in alignment with internal data governance policies and emerging AI regulations.

• Mitigate AI-Assisted Development Risks: Critically evaluate and audit AI-generated code to prevent security vulnerabilities, logic flaws, and technical debt, ensuring deep technical understanding rather than over-reliance on automated code generation.

Education Qualifications:

• Bachelor’s Degree or higher in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, or related technical field.

Professional Experience:

• 1 to 4 years of hands-on experience in AI/ML development, or an equivalent record of strong personal projects, research, or internships building applied AI/LLM solutions.

Technical Skillset:

• Programming: Strong proficiency in Python for building AI/ML applications, including working with LLM APIs/SDKs and common AI libraries, as well as practical experience in data manipulation (e.g., using Pandas or SQL to clean and prepare data).

• LLM Development: Hands-on experience building LLM-powered applications, including prompt design, tool/function calling, and connecting models to external data and tools.

• Retrieval & Vector Databases: Familiarity with vector databases, embedding models, and chunking/retrieval strategies (such as semantic chunking, hybrid search, and reranking).

• Statistical Modeling & Machine Learning: Solid foundation in applied statistics, probability, and classical machine learning techniques. Proven ability to evaluate trade-offs and select the optimal modeling approach for specific business objectives.

• Cloud Computing: Experience with cloud platforms. AWS is the primary ecosystem for deploying and serving AI workloads, while familiarity with GCP or Azure is a welcome advantage.

• Engineering & LLMOps: Familiarity with version control (Git) and an awareness of general MLOps/LLMOps concepts (such as basic model testing, tracking performance, or managing prompts).