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Skills:

Compliance, Research, Python

Job type:

Full-time

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