Sr Tech Lead-GenAI - VectorDBand PostgreSQL

Job Summary

This role is accountable for delivering advanced Generative AI solutions by leveraging large language models and prompt engineering techniques. The position drives technical excellence in project execution, ensures robust solution design, and optimizes AI‑driven applications using modern Python frameworks and scalable database technologies. The individual acts as a technical authority, guiding teams in implementing best practices, and plays a key role in the successful delivery of complex AI projects aligned with organizational goals.

• Provide advanced proficiency in GenAI and prompt engineering by designing, refining, and deploying LLM‑driven solutions using Python frameworks such as Flask, Django, and FastAPI.
• Architect and implement RESTful APIs to integrate LLM models and vector databases (e.g., Pinecone, PostgreSQL, AzureAISearch) for scalable and efficient data retrieval in AI applications.
• Optimize database schemas and embeddings using PostgreSQL and VectorDB to enhance performance and accuracy of generative AI systems.
• Oversee code quality and performance by conducting comprehensive code reviews and enforcing best practices in Python, RESTful API development, and prompt engineering.
• Lead technical feasibility studies and solution breakdowns, evaluating architecture alternatives and technical risks for GenAI project modules.
• Collaborate with internal stakeholders to define technical objectives, deliverables, and ensure process compliance in the development and deployment of AI‑powered solutions.

• Strong hands‑on experience in building AI‑enabled applications or solutions.
• Good practical knowledge of LLM‑based application patterns such as prompt design, structured outputs, RAG, and semantic/ vector search.
• Strong Python development skills.
• Good experience with SQL and data analysis / comparison logic.
• Experience in building APIs, services, or backend components for AI solutions.
• Ability to convert use cases into practical implementation steps.
• Strong communication skills and ability to work with both technical and non‑technical stakeholders.
• Ability to work in an environment where business context, data, and governance matter.
• Experience with Git Hub Copilot or other developer productivity tooling.
• Experience with document‑heavy enterprise AI use cases.
• Experience in migration / transformation support, including code conversion or code understanding use cases.
• Experience in regulated environments such as banking, risk, finance, or compliance.
• Familiarity with production support, validation, reconciliation, or control processes.
• Experience in setting up evaluation and monitoring for AI solutions.
• Technical skills must include Python, SQL, REST APIs, JSON/structured data handling, AI application development using LLMs, prompt engineering, RAG/vector‑search‑based solution patterns.
• Nice‑to‑have skills:
  Databricks, Azure OpenAI / Azure AI services, FastAPI or Flask, Git Hub Copilot, YAML, Dev Ops / CI‑CD exposure, basic frontend integration understanding.

• Optional but valuable:
  Certifications such as Tensor Flow Developer Certificate.
• Microsoft Azure AI Engineer Associate certification (preferred).