Requirements
• 3-5 years of full-time software engineering experience, with strong TypeScript/Node.js skills (NestJS preferred).
• Shipped and owned a conversational, search, or RAG feature used by real users at scale: a customer-facing product, not an internal tool, company knowledge-base bot, or internal RAG.
• Hands-on production experience with LangChain or LangGraph for building agentic or multi-step LLM workflows.
• Hands-on experience with Langfuse for tracing, evaluations, and regression testing of prompt and model changes.
• Search relevance

experience:
built or tuned search with Elasticsearch/OpenSearch and vector search (Qdrant, Pinecone, etc.), and measured relevance rather than eyeballing it.
• Data-driven approach: comfortable defining success metrics, working with product analytics, and using funnel and conversion data to decide what to build or improve. Nice to have
• Built an LLM-as-judge setup or relevance test set that changed a real decision.
• Experience with geo/places data, entity resolution, or local search.
• Experience with reranking and rank fusion.
• Experience with messaging platform APIs (WhatsApp Cloud API, Instagram Graph API).
• Experience with prompt caching or model routing at scale.
• Exposure to multimodal understanding (video, images).
• Exposure to Model Context Protocol (MCP).
• Prior experience in a quick-paced, AI-driven startup environment. Responsibilities




• Build agentic, multi-step LLM workflows: orchestration, tool calling, memory, and multi-turn conversation state.
• Own our conversational AI end to end across in-app chat: from product requirement, to production, to iteration based on real user behavior.
• Own retrieval quality for our hybrid search and RAG systems, grounding every answer in first-party data.
• Work on query understanding and entity resolution for location-aware search.
• Make LLM features reliable in production: structured outputs, streaming, fallbacks, and routing across multiple models based on quality, latency, and cost.
• Build async, event-driven pipelines that hold up under real user traffic.
• Build the evaluation and observability layer: tracing, eval datasets curated from real user conversations, regression testing for prompt, model, and retrieval changes, and metrics for relevance and groundedness.
• Optimise cost and latency across LLM workloads.
• Identify the data a feature needs, design and architect the system around it, and source it from the right teams: product analytics, conversion and growth metrics.
• Translate requirements and product insights into shipped features, collaborating closely with the founder, engineering, and product. Job Details Mumbai, work from office. Interview Process
• Initial Screening
• Technical Interview
• Founder Discussion

📌 Artificial Intelligence Engineer (Mumbai)
🏢 ClanX
📍 Mumbai