• Design and deploy agentic generative AI solutions leveraging LLMs, RAG pipelines, context engineering, and REST APIs. • Architect and build multi-agent automation systems using frameworks such as LangGraph or similar orchestration platforms • Implement LLM governance architecture including prompt drift detection, auditability, and safety policies to ensure regulatory compliance. • Develop and integrate vector database solutions for semantic search and retrieval-augmented generation. • Collaborate cross-functionally with AI architects, product managers, and compliance teams to accelerate AI adoption across business units. • Develop APIs using FastAPI/Flask and build interactive demos for stakeholder showcases. • Contribute to MLOps practices including CI/CD pipelines, model monitoring, and deployment automation. • Conduct research experiments to enhance model performance, reliability, and fairness. • Build and maintain ML models for domain-specific tasks such as NLP, computer vision, and predictive analytics. ### Requirements • Programming: Proficiency in Python; working knowledge of SQL and Java. • GenAI & LLMs: Deep expertise in LLMs, RAG, prompt engineering, fine-tuning, and model governance. • Frameworks: LangChain, • APIs & Backend: FastAPI, Flask, REST API design. • Databases: Vector databases (Milvus, PGVector), PostGRESQL. • Cloud & MLOps: Azure AI Foundry; Docker, Kubernetes, CI/CD pipelines. • Visualization & UI: Streamlit, basic UI/UX design principles.