{"id":4091249,"url":"https://alion.io/job/clanx-artificial-intelligence-engineer","title":"Artificial Intelligence Engineer","company":{"id":673123,"name":"Clanx","domain":"clanx.com","url":"https://alion.io/company/clanx","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ElasticSearch","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Nest.JS","optional":false},{"name":"Node JS","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"TypeScript","optional":false},{"name":"Function Calling","optional":true},{"name":"Hybrid Search","optional":true},{"name":"JavaScript","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Prompt Caching","optional":true},{"name":"Reranking","optional":true},{"name":"Structured Outputs","optional":true},{"name":"WhatsApp","optional":true}],"status":"live","first_seen_at":"2026-11-11T17:20:19Z","employer_posted_date":null,"last_verified_at":"2026-11-11T17:20:19Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Requirements\n3-5 years of full-time software engineering experience, with strong TypeScript/Node.js skills (NestJS preferred).\nShipped 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.\nHands-on production experience with LangChain or LangGraph for building agentic or multi-step LLM workflows.\nHands-on experience with Langfuse for tracing, evaluations, and regression testing of prompt and model changes.\nSearch relevance experience: built or tuned search with Elasticsearch/OpenSearch and vector search (Qdrant, Pinecone, etc.), and measured relevance rather than eyeballing it.\nData-driven approach: comfortable defining success metrics, working with product analytics, and using funnel and conversion data to decide what to build or improve.\n\nNice to have\nBuilt an LLM-as-judge setup or relevance test set that changed a real decision.\nExperience with geo/places data, entity resolution, or local search.\nExperience with reranking and rank fusion.\nExperience with messaging platform APIs (WhatsApp Cloud API, Instagram Graph API).\nExperience with prompt caching or model routing at scale.\nExposure to multimodal understanding (video, images).\nExposure to Model Context Protocol (MCP).\nPrior experience in a fast-paced, AI-driven startup environment.\n\nResponsibilities\nBuild agentic, multi-step LLM workflows: orchestration, tool calling, memory, and multi-turn conversation state.\nOwn our conversational AI end to end across in-app chat: from product requirement, to production, to iteration based on real user behavior.\nOwn retrieval quality for our hybrid search and RAG systems, grounding every answer in first-party data.\nWork on query understanding and entity resolution for location-aware search.\nMake LLM features reliable in production: structured outputs, streaming, fallbacks, and routing across multiple models based on quality, latency, and cost.\nBuild async, event-driven pipelines that hold up under real user traffic.\nBuild 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.\nOptimise cost and latency across LLM workloads.\nIdentify 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.\nTranslate requirements and product insights into shipped features, collaborating closely with the founder, engineering, and product.\n\nJob 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