Must-have qualifications 6+ years in software engineering, with 2+ years building GenAI/LLM applications in production. Solid Python (incl. async) and Java (the primary enterprise application stack; Spring a plus); solid API and systems design. Azure GenAI hands-on: Azure OpenAI, Azure AI Foundry, Azure AI Search for RAG, Azure AI Document Intelligence (IDP), and Prompt Flow. Agent frameworks: Microsoft Agent Framework / Semantic Kernel / AutoGen (or LangChain / LangGraph) and tool / function calling. RAG fundamentals: embeddings, chunking, vector search, reranking, and grounding. Data platforms: Snowflake including Cortex AI (Cortex Search, LLM functions) and SQL, for accessing and grounding on enterprise data. Prompt engineering as versioned code; building and running evaluations. DevOps: Azure DevOps / GitHub Actions, Docker,



AKS / Azure Functions, and observability. Preferred Financial services or other regulated environments. Front-end (React) for AI-assisted UX; streaming and tokenlevel operations. Azure AI Content Safety and responsible-AI practices. Certification: Azure AI Engineer Associate. What success looks like - first 6 to 12 months Multiple GenAI features shipped to production within the embedded delivery pods. Measurable adoption and productivity uplift in the teams you support. Reusable components adopted from the architects' reference framework. Clear contributio

Skills:- Python

📌 Urgent Requirement -AI ImplementationEngineer (Mumbai)
🏢 wissen technology
📍 Mumbai