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Mid-level Bengaluru role but specialized GenAI skills reduce general applicant density.
Specialized GenAI/LLM frameworks, vector databases, and Azure AI focus make background fit highly domain-sensitive.
Explicit years plus mandatory LangChain, RAG, vector DB and Azure AI skills impose strict technical filters.
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Develop and maintain LLM-powered components and multi-agent orchestration workflows for supply chain automation.
Design and optimize Retrieval-Augmented Generation (RAG) pipelines and implement context graph solutions for enhanced LLM reasoning within supply chain domains.
Integrate and manage Azure AI services (OpenAI, Cognitive Search, ML) and ensure production readiness including prompt engineering, hallucination mitigation, and incident resolution.
2–5 years software engineering experience; at least 1–2 years in GenAI / LLM application development.
Proficiency in Python and/or TypeScript/JavaScript for backend and frontend AI services.
Hands-on experience with LangChain and/or LangGraph, and working knowledge of Azure AI ecosystem including Azure OpenAI Service and Azure AI Search.
Strong understanding of RAG architecture, multi-agent systems, context graphs, prompt engineering, and LLM evaluation techniques.
Experienced full-stack AI engineer with demonstrated ability to build production-grade GenAI solutions in supply chain or similar domains.
Comfortable working autonomously to resolve incidents, manage model versions, and optimize agent workflows end-to-end.
Skilled in applying advanced LLM orchestration frameworks and Azure AI services to build scalable, multi-agent AI systems with considerations for security and responsible AI.