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Tier-2 brand, popular mid-level ML role, metro hiring, broad GenAI stack increases competition.
Specialized GenAI and vector-search expertise moderately limits transferability across non-ML roles.
Requires specific RAG, LangChain, vector DB and Python backend expertise, enforcing strict technical filters.
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Design, develop, and maintain scalable Python backend services for GenAI systems using frameworks like FastAPI or Flask.
Build and manage end-to-end Retrieval-Augmented Generation (RAG) systems including data ingestion, embeddings, retrieval, and response grounding.
Implement and operate agentic AI workflows using LangChain and LangGraph, integrating vector databases such as FAISS, Pinecone, and Weaviate for semantic search.
Strong experience in Python backend development.
Experience with Retrieval-Augmented Generation (RAG) systems and vector search technologies (FAISS, Pinecone, Weaviate).
Familiarity with frameworks such as FastAPI or Flask and AI tooling like LangChain, LangGraph.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building production-grade large language model (LLM) pipelines with end-to-end data workflows.
Comfortable collaborating across product, data, and infrastructure teams to deliver scalable AI systems.
Skilled in writing tests, enforcing quality checks, and ensuring production readiness via CI/CD pipelines.