Lead AI or ML Engineer

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. We are seeking a highly experienced Lead Applied Data Scientist to lead the discovery, design, and adoption of AI-powered solutions across Machine Learning, Deep Learning, Generative AI, and Agentic AI domains. This role serves as a senior technical leader responsible for driving innovation, solving complex business problems, defining scalable AI solution strategies, and accelerating the transition of AI solutions from experimentation to production. The position combines deep hands-on technical expertise with the ability to lead small teams, mentor scientists, and influence enterprise AI direction. Owns AI strategy, architecture decisions, enterprise standards, reusable frameworks, capability development, and leadership of small teams while remaining deeply hands-on in solving critical business challenges. Primary Responsibilities: AI Strategy & Technical Leadership Drive AI solution strategy for complex and high-impact business problems Lead technical design, solution architecture, and AI technology selection decisions Establish reusable AI patterns, frameworks, standards, and best practices Provide technical leadership and mentorship to Applied Data Scientists and cross-functional teams Evaluate emerging AI technologies and recommend enterprise adoption approaches Applied AI Solution Development Translate business challenges into scalable AI, ML, GenAI, and Agentic AI solutions Design and develop POCs, prototypes, and reference implementations Extensive experience in areas by providing solutions using machine learning, deep learning, NLP, recommendation systems, forecasting, multimodal AI, and Generative AI techniques Build reusable assets including prompts, workflows, evaluation frameworks, and implementation accelerators Define production-ready solution blueprints to support engineering adoption Generative AI & Agentic AI Design and implement solutions using LLMs, RAG, semantic search, vector retrieval, and prompt engineering techniques Develop agentic workflows leveraging orchestration frameworks, tool integration, planning, reasoning, and multi-agent collaboration Establish evaluation, guardrail, and governance frameworks for GenAI applications Optimize solution performance, quality, and cost efficiency Production Readiness & MLOps Drive successful transition of validated AI solutions into production environments Apply AI Development Lifecycle (AIDLC) and MLOps best practices across experimentation and deployment Ensure scalability, observability, monitoring, model governance, and operational readiness Collaborate with engineering and platform teams to operationalize AI solutions Team & Organizational Impact Lead a small team of Applied Data Scientists while remaining hands-on Mentor team members on AI methodologies, experimentation practices, and technical excellence Promote knowledge sharing, innovation, and adoption of reusable AI capabilities Influence enterprise AI strategy, architecture standards, and capability development Comply 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 Required Qualifications: Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Artificial Intelligence, or related field; Master's preferred 15+ years of experience delivering enterprise AI/ML solutions Hands-on experience with Generative AI technologies including LLMs, RAG, prompt engineering, embeddings, vector databases, agentic systems Experience developing Agentic AI solutions using orchestration frameworks and tool-enabled workflows Experience with Azure ML, SageMaker, Vertex AI, MLflow, Docker, Kubernetes, and cloud-native AI platforms Experience mentoring data scientists and leading small technical teams Proven experience leading complex AI initiatives from ideation through production deployment Expertise in Python, SQL, PyTorch, and/or TensorFlow Solid expertise in machine learning, deep learning, statistical modeling, predictive analytics, and experimentation Solid knowledge of AI architecture, MLOps, model governance, and production AI systems Demonstrated solid communication, stakeholder management, and technical leadership skills Preferred Qualifications: PhD or advanced degree in AI, ML, Computer Science, Statistics, Mathematics, or related discipline Experience building enterprise-scale Generative AI and Agentic AI platforms Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar frameworks Experience designing enterprise AI platforms, feature stores, model registries, and reusable AI services Experience with distributed training, GPU optimization, inference acceleration, and model optimization techniques Experience establishing Responsible AI, governance, risk management, and compliance frameworks Healthcare experience involving Claims, EHR/FHIR, Clinical Analytics, Care Management, or Population Health Contributions to patents, publications, AI accelerators, or innovation programs Technical Skills AI/ML & Analytics: Machine Learning, Deep Learning, Statistical Modeling, Predictive Analytics Applied AI Modeling: Natural Language Processing (NLP), Computer Vision, Time Series Forecasting, Recommendation Systems, Anomaly Detection Generative & Agentic AI: Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, Agentic AI, Multi-Agent Systems, Semantic Search AI Frameworks: PyTorch, TensorFlow, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, LangGraph Programming & Data Engineering: Python, SQL, Feature Engineering, Data Pipelines, Model Evaluation, Experimentation Frameworks MLOps & Model Lifecycle: MLOps, MLflow, Kubeflow, CI/CD, Model Monitoring, Observability, Drift Detection, Model Registry, Deployment Automation AI Deployment Platforms: Azure Machine Learning, AWS SageMaker, Google Vertex AI, Docker, Kubernetes, API-Based Model Serving Cloud & Infrastructure: Azure, AWS, GCP, Cloud-Native AI, Hybrid Cloud, On-Premises Deployments Vector & Knowledge Systems: Pinecone, FAISS, Weaviate, Chroma, pgvector, Azure AI Search, Neo4j Data Platforms: Databricks, Snowflake, BigQuery, Kafka, Lakehouse Architectures AI Architecture & Engineering: Enterprise AI Architecture, Scalable ML Systems, AI Solution Design, Feature Stores, AI Platforms, Production AI Deployment Responsible AI & Governance: Responsible AI, Explainability, Fairness, AI Governance, Risk Management, SOC 2, HITRUST, HIPAA Healthcare Analytics: Healthcare Analytics, Claims, Clinical Data, EHR/HL7/FHIR, ICD/CPT, Risk Adjustment, Population Health, Care Management Leadership & Strategy: Technical Leadership, AI Strategy, Innovation, Stakeholder Management, Mentoring, Cross-Functional Collaboration 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.