The Role
Designs and leads enterprise AI/ML, Generative AI, LLM, RAG, and agentic AI solutions from architecture through production. Builds Azure-based AI systems, APIs, microservices, MLOps and LLMOps practices, evaluation frameworks, security controls, and Responsible AI capabilities. Collaborates with stakeholders to identify AI opportunities, mentors engineers, evaluates foundation models, and drives scalable solutions across business functions.
Summary Generated by Built In
Requisition Number: 2395061
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.
The Senior AI/ML Engineer is responsible for designing, architecting, and implementing enterprise-scale AI solutions involving structured and unstructured data sources (PDFs, documents, databases, APIs, enterprise applications, knowledge repositories, etc.) to develop intelligent systems using Machine Learning, Generative AI, Large Language Models (LLMs), Agentic AI, and advanced AI frameworks. The engineer will be responsible for identifying AI/ML opportunities and leading the design and delivery of AI-powered solutions within Digital, Sales, Marketing, Operations, and Enterprise platforms to improve customer experience, automate business processes, enhance productivity, generate actionable insights, and support strategic decision-making. The candidate should possess Solid expertise in Python, Machine Learning, Generative AI technologies, AI architecture, and cloud-native AI platforms, with the ability to lead AI initiatives from concept through production deployment.
Primary Responsibilities:
Required Qualifications:
Preferred Qualifications:
At 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. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
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.
The Senior AI/ML Engineer is responsible for designing, architecting, and implementing enterprise-scale AI solutions involving structured and unstructured data sources (PDFs, documents, databases, APIs, enterprise applications, knowledge repositories, etc.) to develop intelligent systems using Machine Learning, Generative AI, Large Language Models (LLMs), Agentic AI, and advanced AI frameworks. The engineer will be responsible for identifying AI/ML opportunities and leading the design and delivery of AI-powered solutions within Digital, Sales, Marketing, Operations, and Enterprise platforms to improve customer experience, automate business processes, enhance productivity, generate actionable insights, and support strategic decision-making. The candidate should possess Solid expertise in Python, Machine Learning, Generative AI technologies, AI architecture, and cloud-native AI platforms, with the ability to lead AI initiatives from concept through production deployment.
Primary Responsibilities:
- Design, architect, develop, and implement scalable AI/ML, Generative AI, and Agentic AI solutions to solve complex business problems
- Lead the development of enterprise AI architectures involving traditional machine learning models, LLMs, Retrieval-Augmented Generation (RAG), vector databases, and autonomous agent systems
- Design and implement intelligent multi-agent workflows using Microsoft Azure AI Agents Framework, Azure AI Foundry, Semantic Kernel, LangChain, LangGraph, and similar agent orchestration frameworks
- Collaborate with business stakeholders, product owners, architects, and engineering teams to identify and prioritize AI/ML opportunities across the organization
- Develop and deploy production-grade AI solutions utilizing Azure OpenAI Service, Azure AI Search, Azure Machine Learning, and related cloud AI services
- Create robust RAG solutions leveraging embeddings, semantic search, knowledge retrieval, and enterprise data sources
- Design and implement prompt engineering strategies, prompt orchestration patterns, and advanced reasoning workflows to improve AI model effectiveness
- Establish AI evaluation and benchmarking frameworks to assess model quality, accuracy, groundedness, relevance, latency, cost efficiency, and business value
- Conduct comparative evaluation of foundation models and LLMs to determine optimal solutions for business use cases
- Develop scalable APIs, microservices, and reusable AI components for enterprise adoption
- Implement MLOps and LLMOps practices including model deployment, monitoring, observability, CI/CD automation, and lifecycle management
- Ensure AI solutions meet enterprise standards for scalability, security, reliability, performance, and maintainability
- Lead Responsible AI implementation including fairness, transparency, explainability, accountability, privacy, and governance requirements
- Design and implement AI security controls including prompt injection protection, data leakage prevention, access controls, secure model deployment, and AI threat mitigation strategies
- Conduct technical reviews, provide mentorship to engineering teams, and drive adoption of AI best practices across the organization
- Stay current with evolving AI technologies, foundation models, agent frameworks, and enterprise AI trends to drive innovation
- Builder Responsibilities:
- Design, develop, and deploy AI-powered solutions using no-code, low-code, and advanced platforms, translating business needs into scalable applications that enhance products, workflows, and decision-making.
- Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements
Required Qualifications:
- Overall 10+ years of software Engineering Experience, 6+ years of experience in Python development using common AI/ML and data science libraries such as Scikit-Learn, Pandas, PyTorch, TensorFlow, and Databricks
- 5+ years of experience applying Machine Learning, NLP, and Deep Learning techniques in production environments using large-scale enterprise datasets
- 3+ years of experience designing and implementing Generative AI solutions utilizing Large Language Models (GPT, Claude, Gemini, Llama, Mistral, etc.)
- 3+ years of experience building enterprise RAG solutions using embeddings, vector databases, semantic search, and knowledge retrieval techniques
- Hands-on experience designing and implementing Agentic AI solutions using Microsoft Azure AI Agents Framework, Azure AI Foundry
- Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks
- Solid understanding of LLM orchestration, planning, memory management, tool calling, agent communication, and multi-agent architectures
- Experience applying data science techniques in a commercial enterprise, including supervised learning, unsupervised learning, NLP, deep learning, forecasting, optimization, recommendation systems, and predictive analytics
- Experience designing, developing, and deploying scalable APIs and microservices for AI applications
- Solid experience in cloud platforms (Azure preferred) with expertise in Azure OpenAI Service, Azure AI Search, Azure Machine Learning, Azure Databricks, Azure Functions, and Azure Kubernetes Service (AKS)
- Experience implementing MLOps and LLMOps practices including model governance, CI/CD pipelines, monitoring, observability, and automated deployment processes
- Solid understanding of AI evaluation frameworks, benchmarking methodologies, and model quality assessment techniques
- Experience with Prompt Engineering, Prompt Optimization, Prompt Chaining, ReAct, Chain-of-Thought, and advanced prompting techniques
- Experience implementing AI security best practices, including prompt injection prevention, model security, data privacy, and secure AI architectures
- Solid knowledge of Responsible AI principles, AI governance frameworks, and model explainability concepts
- Solid written and verbal communication skills, including the ability to present technical solutions and architecture recommendations to both technical and business stakeholders
- Organized self-starter with proven leadership skills, Solid problem-solving ability, and the capability to lead complex AI initiatives with minimal supervision
- Solid analytical, quantitative, critical thinking, and decision-making skills
Preferred Qualifications:
- Experience with multimodal AI solutions involving Vision, OCR, Speech, Audio, and Document Intelligence models
- Experience fine-tuning foundation models and deploying custom AI models
- Experience with AI evaluation and observability tools such as LangSmith, Prompt Flow, RAGAS, DeepEval, TruLens, and Azure AI Evaluation
- Experience building Enterprise AI Copilots and autonomous AI assistants
- Experience working in Healthcare, Insurance, Financial Services, or other regulated industries
- Microsoft Azure certifications such as AI-102, DP-100, Azure Solutions Architect Expert, or related AI/Cloud certifications
- Experience contributing to AI Centers of Excellence (CoE), AI Governance Boards, or enterprise AI transformation initiatives
- Experience designing Proof of Concepts (PoCs), AI accelerators, and reusable AI platforms for enterprise-scale adoption
At 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. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
Skills Required
- 10+ years of overall software engineering experience
- 6+ years of Python development experience using AI/ML and data science libraries
- Experience with Scikit-Learn, Pandas, PyTorch, TensorFlow, and Databricks
- 5+ years applying machine learning, NLP, and deep learning techniques in production environments
- 3+ years designing and implementing Generative AI solutions using large language models
- 3+ years building enterprise RAG solutions using embeddings, vector databases, semantic search, and knowledge retrieval
- Hands-on experience designing and implementing agentic AI solutions
- Experience with Azure AI Agents Framework, Azure AI Foundry, Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks
- Understanding of LLM orchestration, planning, memory management, tool calling, agent communication, and multi-agent architectures
- Experience applying supervised learning, unsupervised learning, NLP, deep learning, forecasting, optimization, recommendation systems, and predictive analytics
- Experience designing, developing, and deploying scalable APIs and microservices for AI applications
- Experience with cloud platforms, preferably Azure
- Expertise with Azure OpenAI Service, Azure AI Search, Azure Machine Learning, Azure Databricks, Azure Functions, and Azure Kubernetes Service
- Experience implementing MLOps and LLMOps, including governance, CI/CD, monitoring, observability, and automated deployment
- Understanding of AI evaluation frameworks, benchmarking methodologies, and model quality assessment
- Experience with prompt engineering, prompt optimization, prompt chaining, ReAct, Chain-of-Thought, and advanced prompting
- Experience implementing AI security practices, including prompt injection prevention, model security, data privacy, and secure AI architectures
- Knowledge of Responsible AI principles, governance frameworks, and model explainability
- Strong written and verbal communication skills for technical and business audiences
- Leadership, problem-solving, analytical, quantitative, critical-thinking, and decision-making skills
- Experience with multimodal AI involving vision, OCR, speech, audio, or document intelligence
- Experience fine-tuning foundation models and deploying custom AI models
- Experience with LangSmith, Prompt Flow, RAGAS, DeepEval, TruLens, or Azure AI Evaluation
- Experience building enterprise AI copilots and autonomous AI assistants
- Experience in healthcare, insurance, financial services, or another regulated industry
- Microsoft Azure certifications such as AI-102, DP-100, or Azure Solutions Architect Expert
- Experience contributing to AI Centers of Excellence, AI Governance Boards, or enterprise AI transformation initiatives
- Experience designing proof of concepts, AI accelerators, and reusable AI platforms
Optum Compensation & Benefits Highlights
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Leave & Time Off Breadth — Paid time off is often regarded as generous, with meaningful accrual and flexibility to use it. While practices can vary by team, the overall time-off offering stands out as a relative strength.
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Retirement Support — A 401(k) plan with employer matching is available and generally well-regarded for supporting long-term savings. It remains valuable, though candidates should verify the current match formula.
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Parental & Family Support — Programs such as parental leave, family medical leave, and adoption assistance are included as notable elements of the total rewards package. These resources can meaningfully support work-life balance when applicable to the role and location.
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The Company
What We Do
Optum, part of the UnitedHealth Group family of businesses, 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. At Optum, we support your well-being with an understanding team, extensive benefits and rewarding opportunities. By joining us, you’ll have the resources to drive system transformation while we help you take care of your future. We recognize the power of connection to drive change, improve efficiency and make a difference in health care. Join a team where your skills and ideas can make an impact and where collaboration is key to creating technology that produces healthier outcomes.
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Optum Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Optum has three workplace models that balance the needs of the business and the responsibilities of each role. These models, core on‑site (5 days/week), hybrid (4 days/week) and telecommute or fully remote, vary by country, role and location.
Typical time on-site:
Not Specified
