Senior Gen AI Engineer

Senior GenAI Engineer – Applied LLMs

Location: Mumbai / Pune
Job Type: Full-time
Experience: 4–7 years

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.

• Design and deploy real‑world GenAI applications, including RAG systems, copilots, and agentic workflows.
• Build applications using both commercial APIs (OpenAI, Anthropic Claude, Google Gemini) and open‑weight models (Llama, Mistral, Deep Seek).
• Architect robust retrieval pipelines using vector databases such as Pinecone, Qdrant, Weaviate, or pgvector.
• Handle advanced prompt engineering, manage structured outputs (JSON mode), and implement function/tool calling.
• Set up rigorous evaluation frameworks (like LLM‑as‑a‑judge or RAGAS) to actively monitor accuracy, hallucinations, latency, and costs.
• Write clean, well‑documented code while collaborating with product teams and mentoring junior engineers.

• 4–7 years of overall engineering or data science experience, with at least 1.5+ years deeply focused on building GenAI applications for production.
• Strong proficiency in Python and modern AI libraries like Lang Chain, Llama Index, or Hugging Face.
• Real‑world, hands‑on experience with RAG pipelines, chunking strategies, embeddings, and hybrid search.
• A solid understanding of LLM quirks—including how to manage context windows and mitigate prompt leakage or drift.
• Experience deploying services on at least one major cloud provider (AWS, Azure, or GCP) using Docker and CI/CD pipelines.

• Experience designing multi‑agent systems using frameworks like Lang Graph, CrewAI, or Auto Gen.
• Familiarity with fine‑tuning techniques (LoRA, QLoRA) or inference optimization.
• Hands‑on experience with the Model Context Protocol (MCP) and agent‑to‑agent patterns.