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.

Key Responsibilities

1. 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.
2. 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.
3. Optimize database schemas and embeddings using PostgreSQL and VectorDB to enhance performance and accuracy of generative AI systems.
4. Oversee code quality and performance by conducting comprehensive code reviews and enforcing best practices in Python, RESTful API development, and prompt engineering.
5. Lead technical feasibility studies and solution breakdowns, evaluating architecture alternatives and technical risks for GenAI project modules.
6. Collaborate with internal stakeholders to define technical objectives, deliverables, and ensure process compliance in the development and deployment of AI-powered solutions.

Skill Requirements

AI Solutions Expert Vacancy Role purpose We are looking for an AI Solutions Expert to help build practical AI solutions within the Finance & Risk Grid. The role will focus on turning identified use cases into working solutions that improve productivity, analysis, validation, migration, and operational processes across the grid. The expert will work closely with business and IT SPOCs across FR grid, helping teams move from ideas to implementation and establishing reusable patterns for AI adoption within the grid. Key responsibilities Translate business and IT AI use cases into practical solution designs and prototypes Build AI-enabled solutions for areas such as: document and knowledge intelligence data query / validation / comparison support migration and code transformation support regression and reconciliation support production and operational intelligence Work with business and IT SPOCs to understand processes, pain points, and expected outcomes Design and develop AI-assisted solutions using approved tools and platforms Build reusable components and patterns that can scale across projects in the Finance & Risk Grid Support rapid pilots, proofs of concept, and structured scaling of successful solutions Help define guardrails, quality checks, and practical governance for AI usage Contribute to skill build-up within the grid by coaching teams and sharing implementation knowledge Work closely with architects, platform teams, and AI COE where needed Required profile 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 semantic search / 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 Preferred / desirable experience Experience with GitHub Copilot / 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-have Python SQL REST APIs JSON / structured data handling AI application development using LLMs Prompt engineering for enterprise use cases RAG / vector-search-based solution patterns Nice-to-have Databricks Azure OpenAI / Azure AI services FastAPI or Flask GitHub Copilot YAML DevOps / CI-CD exposure basic frontend integration understanding Ways of working Works in close collaboration with grid AI lead, architects, and business/IT SPOCs Helps structure work into quick wi

Other Requirements

1. Optional but valuable:

2. Certifications such as Tensor Flow Developer Certificate

• Microsoft Azure AI Engineer Associat