{"id":4225506,"url":"https://alion.io/job/tata-consultancy-services-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":222,"name":"Tata Consultancy Services","domain":"tcs.com","url":"https://alion.io/company/tata-consultancy-services","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":100,"open_postings":114,"ghost_share":0.018,"stale_share":0,"repost_share":0.254,"time_to_fill_p50_days":9,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pittsburgh, United 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such as:Retrieval-Augmented Generation (RAG)\nPrompt engineering\nTool/function calling\nAI agents\nModel orchestration\nEmbeddings and vector search\nLLM evaluation and monitoring\nExposure to frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, Hugging Face, or similar.\nExperience working with REST APIs, microservices, data pipelines, and enterprise integration patterns.\nUnderstanding of MLOps/LLMOps, CI/CD pipelines, automated testing, version control, deployment, and model monitoring.\nExperience with Git, Docker, Kubernetes, Jenkins, GitHub Actions, Azure DevOps, or comparable SDLC technologies.\nKnowledge of cloud-based AI/ML platforms such as Microsoft Azure/Azure AI, AWS/SageMaker/Bedrock, or Google Cloud/Vertex AI.\nExperience designing AI/ML solutions within model governance, model risk management, security, privacy, and regulatory frameworks.\nStrong analytical and troubleshooting skills for supporting models and AI applications in production.\nStrong communication and stakeholder-management skills, with the ability to work directly with internal clients to collect requirements and translate them into technical solutions.\nExperience supporting release planning, implementation, production deployments, and post-release activities.\nRoles & Responsibilities:Design, develop, orchestrate, and implement production-grade Machine Learning and Generative AI solutions.\nBuild applications and services leveraging LLMs, Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI frameworks.\nDevelop and integrate AI agents and workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent technologies.\nEvaluate and integrate foundation models and enterprise AI platforms based on business, security, performance, and governance requirements.\nImplement SDLC tooling and engineering harnesses to improve developer productivity, testing, deployment consistency, and overall team efficiency.\nPartner with 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