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Chennai metro and early-mid ML role increase competition, but LLM specialization limits broader applicant pool.
LLM engineering skills are transferable, but RAG and vector DB experience require moderate domain-specific fit.
Mandatory LLM frameworks, vector DB, Python, and production experience create strict technical filters.
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Own end-to-end delivery of LLM-powered application features including design, prototyping, deployment, monitoring, and optimization.
Develop and maintain scalable agent workflows and Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and LLM frameworks like LangChain/LangGraph.
Ensure production readiness focusing on latency, cost, reliability, and observability while collaborating with product, platform, and data teams.
Minimum 2+ years building production software focused on AI-powered or LLM-based applications.
Strong proficiency in Python including API development, backend services, and ML pipelines.
Experience with machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow) and knowledge of ML fundamentals.
Hands-on experience developing LLM applications using LangChain/LangGraph and integrating vector databases (e.g., FAISS, Pinecone).
Experienced engineer skilled in translating business requirements into scalable LLM/AI application solutions.
Proven ability to independently own features and collaborate cross-functionally within product and platform teams.
Comfortable managing production deployment challenges such as CI/CD, Docker, latency optimization, and system observability.