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Mid-level applied AI role in Mumbai with popular LLM skills and metro location, increasing competition.
High specialization in production RAG/LLM pipelines demands ML-specific background, limiting transferability.
Explicit 3–6 years production NLP/ML requirement plus mandatory LLM/RAG tooling tightens filters.
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Design and own end-to-end Retrieval-Augmented Generation (RAG) pipelines, including retrieval architecture, chunking, re-ranking, and prompt design.
Define quality metrics, build evaluation frameworks, and track pipeline health to ensure continual measurable improvement.
Integrate multi-step reasoning, agent workflows, and LLM orchestration considering latency and cost, collaborating with backend engineers for production-grade deployment.
Bachelor's or Master's degree in Computer Science or related field, or equivalent practical experience.
3–6 years of production NLP/ML experience with shipped models or pipelines used by real users.
Hands-on experience building and improving production-grade RAG systems and defining quality metrics for LLM output evaluation.
Proficient in Python engineering with production-grade code; experience with LangChain, LlamaIndex, FastAPI, and LLMs like GPT-4 or Anthropic Claude; familiarity with vector databases (Pinecone, ElasticSearch, pgvector) and cloud platforms (AWS or Azure).
Experienced in deploying complex AI/NLP solutions with a focus on retrieval and generation pipeline reliability and measurable quality improvements.
Operates at the intersection of research and production by continuously translating state-of-the-art research into shipping valuable features.
Comfortable working alongside backend teams to deliver scalable, low-latency AI services in a B2B SaaS environment with a bias towards practical, production-ready solutions.