{"id":1804763,"url":"https://alion.io/job/unitedhealth-group-senior-full-stack-engineer-2","title":"Senior Full Stack Engineer","company":{"id":1043512,"name":"UnitedHealth Group","domain":"unitedhealthgroup.com","url":"https://alion.io/company/unitedhealthgrp","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Radancy","truth_index":{"grade":"B","score":75,"open_postings":95,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16500,"max_usd":39000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":159},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AutoGen","optional":true},{"name":"CrewAI","optional":true},{"name":"Databricks","optional":true},{"name":"Docker","optional":true},{"name":"ETL/ELT","optional":true},{"name":"FastAPI","optional":true},{"name":"Flask","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"PyTorch","optional":true},{"name":"Rest API","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Semantic Search","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-06-29T00:00:00Z","employer_posted_date":"2026-06-29","last_verified_at":"2026-10-24T14:04:54Z","board_verified":true,"closed_at":null,"days_open":117,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":117},"description":"Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.\nWe're looking for a hands-on AI/ML Engineer to design, build, deploy, and support AI solutions using an AI Development Lifecycle (AIDLC) approach. This role focuses on transforming AI/ML, Generative AI, and Agentic AI solutions into scalable, secure, and production-ready applications.\nThe ideal candidate combines solid machine learning fundamentals, software engineering skills, and experience with modern AI platforms. You will work closely with Data Scientists, Applied Scientists, Data Engineers, and Platform Engineers to operationalize AI capabilities and support enterprise AI adoption.\nPrimary Responsibilities:\nAI/ML Solution Development\nDevelop and deploy AI/ML solutions following AI Development Lifecycle (AIDLC) practices\nTranslate business requirements into scalable AI-powered solutions\nBuild, train, evaluate, and deploy machine learning models for enterprise use cases\nSupport the development of production-grade AI systems including:Data ingestion and processing pipelines\nFeature engineering workflows\nModel training and evaluation pipelines\nModel deployment and monitoring solutions\n\nContribute to solution experimentation, validation, and continuous improvement\nAI Engineering & Production Deployment\nDevelop AI services and applications using:APIs and microservices\nBatch and real-time processing patterns\nCloud-native deployment architectures\n\nImplement deployment automation and operational workflows that support reliable AI delivery\nSupport model lifecycle management including deployment, monitoring, retraining, and version control\nContribute to reusable engineering frameworks, templates, and automation assets\nGenerative AI & Agentic AI\nBuild and integrate Generative AI capabilities into enterprise applications and workflows\nSupport solutions leveraging:Large Language Models (LLMs)\nRetrieval-Augmented Generation (RAG)\nEmbeddings and vector search\nPrompt engineering\nSemantic retrieval\n\nAssist with development of Agentic AI workflows involving:Multi-step task execution\nTool and API integrations\nHuman-in-the-loop processes\n\nSupport evaluation and optimization of AI response quality and operational performance\nMLOps, Monitoring & Reliability\nImplement MLOps and LLMOps practices including:Experiment tracking\nDeployment automation\nCI/CD integration\nMonitoring and observability\n\nMonitor AI systems for:Performance\nAccuracy\nDrift\nReliability\nOperational health\n\nParticipate in troubleshooting, root cause analysis, and production support activities\nSupport implementation of logging, monitoring, tracing, and alerting capabilities\nData & Platform Collaboration\nPartner with data engineering teams to ensure data readiness, quality, and reliability\nCollaborate with AI, platform, and product teams to deploy and operate AI solutions at scale\nIntegrate AI capabilities into enterprise systems, applications, and business workflows\nContribute to scalable architecture patterns supporting AI adoption across the organization\nSecurity, Governance & Responsible AI\nFollow Responsible AI, governance, security, and compliance requirements\nSupport model validation, explainability, and auditability activities\nAdhere to enterprise engineering standards, development practices, and operational controls\nComply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so\nRequired Qualifications:\nBachelor's degree in computer science, Engineering, Data Science, Artificial Intelligence, Mathematics, or related field\n5+ years of experience in AI/ML Engineering, Data Science, Software Engineering, or related disciplines\nExperience building and deploying machine learning solutions in enterprise or cloud environments\nExperience developing AI pipelines including data preparation, model training, evaluation, and deployment\nExperience developing APIs, microservices, and cloud-based AI services\nExperience with cloud platforms such as Azure, AWS, and/or GCP\nExperience with Generative AI technologies including:Large Language Models (LLMs)\nRetrieval-Augmented Generation (RAG)\nEmbeddings\nPrompt Engineering\n\nSolid understanding of:Machine Learning\nStatistical Modeling\nFeature Engineering\nModel Evaluation\nAI/ML Lifecycle Management\n\nUnderstanding software engineering principles, testing practices, and production operations\nFamiliarity with MLOps practices including monitoring, deployment automation, and model lifecycle management\nSolid programming skills in Python and SQL\nProven solid analytical, problem-solving, communication, and collaboration skills\nPreferred Qualifications:\nExperience building and deploying production of AI applications and services\nExperience with MLOps and AI platforms such as MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, or similar technologies\nExperience with Generative AI and Agentic AI application development\nExperience implementing RAG solutions, semantic search, vector databases, and knowledge retrieval architectures\nExperience with distributed data processing and large-scale data platforms including Spark, Databricks, Kafka, and cloud-native data services\nExperience implementing monitoring, observability, and operational support practices for AI systems\nExperience contributing to reusable AI frameworks, engineering accelerators, and platform capabilities\nExperience working within healthcare, financial services, insurance, or other regulated industries\nExperience mentoring junior engineers and contributing to engineering best practices\nFamiliarity with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or equivalent platforms\nUnderstanding of Responsible AI, model governance, security, and compliance requirements\nTechnical Skills\nProgramming: Python, SQL\nBackend & APIs: FastAPI, Flask, REST APIs, microservices\nAI/ML: Supervised/unsupervised models, basic deep learning (PyTorch / TensorFlow)\nGenerative AI: LLM APIs, prompt engineering, RAG pipelines, embeddings\nAgentic AI: Basic orchestration, tool integration (guided usage)\nData Handling: Feature engineering, ETL pipelines, data preprocessing\nCloud Platforms: AWS / Azure / GCP (model deployment, storage, APIs)\nMLOps / LLMOps: CI/CD pipelines, model versioning, deployment workflows\nOptimization: Token usage awareness, inference optimization, caching\nMonitoring & Observability: Logging, metrics, model monitoring basics\nContainers & Deployment: Docker, basic Kubernetes exposure\nAt UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. 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