Senior GenAI Engineer – Applied LLMs
Location: Mumbai / Pune
Job Type: Full-time
Experience: 4–7 years
About the Role We are looking for a hands-on Senior GenAI Engineer to help us design, build, and scale production-grade Generative AI systems. We welcome candidates from diverse technical backgrounds-whether you are a software engineer who grew into AI, or a data scientist who transitioned away from classical ML. What truly matters to us is your ability to engineer reliable, trustworthy LLM-powered products from start to finish. If you love working at the application layer of foundation models and turning raw capabilities into secure, well-evaluated products, this role is for you.
What You'll Do
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Design and deploy real-world GenAI applications, including RAG systems, copilots, and agentic workflows.
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Build applications using both commercial APIs (OpenAI, Anthropic Claude, Google Gemini) and open-weight models (Llama, Mistral, DeepSeek).
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Architect robust retrieval pipelines using vector databases such as Pinecone, Qdrant, Weaviate, or pgvector.
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Handle advanced prompt engineering, manage structured outputs (JSON mode), and implement function/tool calling.
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Set up rigorous evaluation frameworks (like LLM-as-a-judge or RAGAS) to actively monitor accuracy, hallucinations, latency, and costs.
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Write clean, well-documented code while collaborating with product teams and mentoring junior engineers.
What We’re Looking For
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4–7 years of overall engineering or data science experience, with at least 1.5+ years deeply focused on building GenAI applications for production.
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Strong proficiency in Python and modern AI libraries like LangChain, LlamaIndex, or Hugging Face.
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Real-world, hands-on experience with RAG pipelines, chunking strategies, embeddings, and hybrid search.
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A solid understanding of LLM quirks-including how to manage context windows and mitigate prompt leakage or drift.
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Experience deploying services on at least one major cloud provider (AWS, Azure, or GCP) using Docker and CI/CD pipelines.
Bonus Points If You Have
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Experience designing multi-agent systems using frameworks like LangGraph, CrewAI, or AutoGen.
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Familiarity with fine-tuning techniques (LoRA, QLoRA) or inference optimization.
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Hands-on experience with the Model Context Protocol (MCP) and agent-to-agent patterns.
