{"id":3451140,"url":"https://alion.io/job/infosys-ai-llm-engineer-2","title":"AI / LLM Engineer","company":{"id":223,"name":"Infosys","domain":"infosys.com","url":"https://alion.io/company/infosys","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"A","score":95,"open_postings":193,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":93,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, 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Learning","optional":true}],"status":"live","first_seen_at":"2026-10-28T15:10:22Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-03T08:07:28Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"As an AI / LLM Engineer, you will build the AI-enabled capabilities that support summaries, narratives, explanations, recommendations, retrieval, evaluation and governed human review experiences across an enterprise platform.\nYou will work closely with architects, product teams, data engineers, backend engineers, security, QA and domain specialists to convert AI patterns into production-ready software components.\nResponsibilities\nDesign and implement LLM-powered workflows for summarization, narrative generation, classification, extraction, contextual reasoning, explanation and reviewer-assist use cases.\nBuild retrieval-augmented generation pipelines including document ingestion, chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning and grounded response generation.\nDevelop reusable prompt templates, prompt versions, context builders, response schemas, evaluation routines and AI orchestration services.\nIntegrate with enterprise AI services such as Azure OpenAI, Azure AI Foundry, OpenAI APIs, Google Gemini, Anthropic, Hugging Face or equivalent approved platforms.\nImplement AI run logging, prompt/model metadata capture, evidence citations, output traceability, reviewer feedback capture and human-in-the-loop controls.\nBuild AI evaluation routines for answer quality, retrieval quality, hallucination checks, regression testing, consistency and groundedness.\nCollaborate with backend and DevOps teams to containerize AI services, deploy them securely, monitor usage, track costs and troubleshoot production issues.\nSupport responsible AI practices such as prompt injection checks, data leakage prevention, policy-based guardrails and AI output validation.\nTechnical requirements\nMinimum 10+ years of experience in software engineering, AI/ML engineering, applied ML, data science engineering or related roles.\nStrong hands-on Python programming experience and practical exposure to LLM-based application development.\nExperience with RAG, vector databases, embeddings, prompt engineering, evaluation frameworks and AI service integration.\nExperience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or equivalent tools.\nWorking knowledge of REST APIs, microservices, SQL, structured data concepts, Git workflows, testing and software engineering practices.\nUnderstanding of document extraction, semantic search, NLP, retrieval quality, hallucination risk, prompt safety and AI evaluation methods.\nAbility to build production-oriented AI components rather than isolated proof-of-concept demos.\nAdditional responsibilities\nExperience with Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure 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