{"id":2446624,"url":"https://alion.io/job/infosys-daai-aiml-engineer","title":"DAAI - AI/ML 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":249,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":93,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CrewAI","optional":false},{"name":"Gemma","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LlamaParse","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"OpenAI","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":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"Weaviate","optional":false},{"name":"Machine Learning","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-12T17:02:26Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T01:26:23Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"As an AI/ML Engineer, you will help build and implement the AI capabilities that power our agentic AI platform. You will work closely with AI/ML architects, founding team, data engineers, and platform engineers to develop agent workflows, retrieval pipelines, evaluation routines, and production-ready AI components.\nThis is a hands-on engineering role for someone who is comfortable building with modern AI/ML and LLM frameworks, agentic AI systems, experimenting with models and prompts, and translating design patterns into working software. The role is ideal for an engineer who wants to work on real-world enterprise AI systems rather than isolated demos\nResponsibilities\nBuild and enhance AI agent workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent technologies.\nImplement AI agents for data discovery, profiling, enrichment, extraction, classification, contextual reasoning, and enterprise knowledge discovery.\nDevelop retrieval-augmented generation pipelines, including document chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning, and response generation.\nWork with vector databases such as Pinecone, Milvus, Qdrant, Weaviate, pgvector, Chroma, or equivalent technologies.\nBuild structured data agents that can connect to databases, inspect schemas, generate SQL, and support semantic understanding of enterprise data.\nImplement document intelligence workflows for PDFs, Word documents, emails, transcripts, logs, and semi-structured enterprise content using Azure Document Intelligence, AWS Textract, LlamaParse, or equivalent tools.\nSupport prompt engineering, prompt testing, prompt versioning, and reusable prompt template development.\nImplement AI evaluation routines covering answer quality, retrieval quality, hallucination checks, regression tests, robustness checks, and response consistency.\nSupport integration with LLM APIs and open-source models from providers such as OpenAI, Anthropic, Azure OpenAI, Hugging Face, Llama, Gemma, or equivalent ecosystems.\nTechnical requirements\nStrong hands-on programming experience in Python and building AI agents.\nPractical exposure to LLMs, RAG, vector databases, embedding models, prompt engineering, and AI application development.\nExperience with one or more AI/LLM frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent tools.\nWorking knowledge of REST APIs, microservices, Git-based development, unit testing, and modern software engineering practices.\nFamiliarity with SQL and structured data concepts, including tables, schemas, joins, relationships, and query generation.\nPreferred skills\nFoundational->Core Engineering->Knowledge Based Engineering,Technology->AI-Data science->Machine Learning,Technology->AI-Data science->Natural Language Processing,Technology->AI-Generative AI->Artificial Intelligence - 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