{"id":3092626,"url":"https://alion.io/job/pocketpills-ai-engineer","title":"AI Engineer","company":{"id":3265,"name":"PocketPills","domain":"pocketpills.com","url":"https://alion.io/company/pocketpills","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"BambooHR","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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Weaviate","optional":false},{"name":"LangSmith","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-22T13:56:04Z","employer_posted_date":null,"last_verified_at":"2026-10-22T13:56:04Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"We're seeking an experienced AI Engineer to design and deploy production-grade LLM applications and AI agents. You'll transform complex AI solutions from prototype through production, building scalable agentic workflows and intelligent systems that drive real-world impact.\nKey Responsibilities\nDesign and implement intelligent agents and workflows using frameworks like LangChain, LangGraph, or similar technologies\n\nEngineer sophisticated multi-step workflows incorporating tool calling, structured outputs, state management, memory systems, and dynamic loops\n\nDevelop robust RAG systems including embeddings, vector search, retrieval optimization, and intelligent reranking\n\nBuild comprehensive evaluation frameworks to assess accuracy, reliability, latency, and cost efficiency\n\nEstablish production-ready observability, guardrails, logging, and error handling mechanisms\n\nOwn the end-to-end journey of AI solutions from initial prototype to full-scale production deployment\n\nRequired Qualifications\nPython proficiency is essential\n\nStrong software engineering fundamentals: OOP principles, asynchronous programming, REST APIs, and robust error handling\n\nProven hands-on experience with LLM and AI agent development\n\nExpertise in prompt engineering, loop design, and agentic graph architecture\n\nAdvanced knowledge of tool calling, function calling, structured outputs, and context/memory management\n\nDeep understanding of RAG systems, embeddings, vector search, and retrieval techniques\n\nProduction experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks\n\nProficiency with REST APIs and backend service architecture\n\nExperience with vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar)\n\nHands-on Docker and cloud infrastructure expertise\n\nPreferred Qualifications\nExperience with LangSmith or equivalent observability platforms\n\nKnowledge of Model Context Protocol (MCP)\n\nExpertise in agent evaluation, benchmarking, and automated testing\n\nFamiliarity with event-driven and asynchronous architectural patterns\n\nExperience\n3-6+ 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