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Niche LLM skills but junior level and Pune metro increase applicant density.
Core ML/AI engineering skills transfer across industries, though LLM/RAG specialization moderately narrows fit.
Explicit 1–3 years plus mandatory LLM/RAG, production deployment, and tech stack increases filter strictness.
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Design, develop, and maintain GenAI applications including chatbots and document/query intelligence systems.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines and orchestrate LLMs using frameworks like LangChain or LlamaIndex.
Develop backend AI services with Python and FastAPI; integrate AI workflows with systems like CRM, databases, and APIs.
1-3 years of work experience in AI or backend development with LLM-based applications.
Proficient in Python backend development and frameworks like FastAPI/Flask/Django.
Experience with LLM orchestration tools (LangChain, LangGraph, LlamaIndex), RAG pipelines, vector databases (FAISS, Pinecone, etc.).
Work location: Pune; Work mode: Work from Office (WFO); Education: BE/BTech/MTech/MCA in CS, AI/ML, Data Science, IT, or equivalent.
Has delivered at least one real-world AI/GenAI application with backend integration and production deployment.
Strong hands-on experience with RAG architecture, prompt engineering, and LLM guardrails to optimize AI response accuracy and safety.
Ability to clearly explain implementation details beyond theoretical AI concepts.