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.
Primary Responsibilities:
- AI native Engineering & Delivery
- Drive the design, development, and rollout of AI/ML and Generative AI capabilities that solve business problems and improve operational outcomes
- Work closely with product, business, data, and architecture partners to define solution approaches and implementation strategies
- Ensure AI-enabled applications meet expectations for performance, scalability, maintainability, and regulatory compliance
- Promote adoption of reusable frameworks, accelerator assets, and engineering best practices to improve delivery efficiency
- Lead execution of engineering roadmaps, release plans, and solution enhancements across multiple initiatives
- ML Ops, Platform Engineering & Cloud Enablement
- Build and enhance enterprise ML Ops capabilities supporting the full AI lifecycle from experimentation through production operations
- Implement automated workflows for model training, validation, deployment, monitoring, rollback, and version management
- Establish model observability practices, including performance monitoring, drift detection, retraining strategies, and operational intelligence
- Improve reproducibility and governance of datasets, prompts, models, features, and experiments
- Partner with platform and infrastructure teams to advance CI/CD, Infrastructure-as-Code, containerization, and deployment automation
- Enable scalable and cost-efficient AI workloads across cloud platforms and modern engineering ecosystems
- Responsible AI & Technical Governance
- Develop and apply standards for responsible and trustworthy AI adoption
- Establish evaluation approaches for model quality, accuracy, explainability, safety, and risk management
- Guide implementation of retrieval, prompting, orchestration, and human oversight patterns for GenAI solutions
- Assess emerging technologies and identify opportunities that create measurable business value
- Contribute to architectural decisions, engineering standards, and technology modernization efforts
- Engineering Leadership & Business Partnership
- Lead and mentor teams of AI/ML engineers, fostering ownership, technical excellence, and continuous learning
- Support talent development through coaching, skill growth, succession planning, and knowledge-sharing initiatives
- Coordinate with cross-functional stakeholders to manage priorities, dependencies, risks, and resource needs
- Provide transparency into delivery progress, operational metrics, adoption trends, and business outcomes
- Influence AI adoption and engineering maturity within the broader organization through collaboration and thought leadership
- Demonstrate adherence to organizational policies, compliance requirements, security standards, and evolving business practices while maintaining a culture of accountability and integrity
- 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:
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, Information Technology, or a related field; equivalent experience considered
- Experience in AI/ML engineering, software engineering, data engineering, or related technical disciplines
- Hands-on experience delivering Generative AI, LLM, AI Agent, Copilot, or intelligent automation solutions
- Experience working within Agile, Scrum, or product-centric delivery models
- Experience with prompt engineering, workflow orchestration, tool integration, and AI solution evaluation
- Experience integrating AI solutions with cloud platforms, APIs, databases, and enterprise applications
- Experience with cloud-native application development and deployment environments
- Working knowledge of RAG architectures, embeddings, vector databases, semantic search, and enterprise knowledge retrieval
- Hands-on knowledge of ML Ops practices including model deployment, model versioning, monitoring, retraining, and lifecycle management
- Understanding software engineering practices including source control, automated testing, CI/CD, and release management
- Understanding of responsible AI, security, governance, compliance, and operational controls for AI solutions
- Solid troubleshooting, problem-solving, communication, and stakeholder management skills
- Solid programming skills in Python with experience building APIs, services, and enterprise-grade applications
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.