Senior Director AI/ML Engineering

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.

As the Senior Director of Software & AI/ML Engineering for GTS, you will drive the strategic vision, architectural direction, and production delivery of scalable, resilient, and secure software and AI/ML solutions that support healthcare applications and workflows. Combining deep software engineering expertise with applied AI/ML leadership, you will build and lead teams delivering cloud-native services, reusable components, and robust APIs alongside machine learning models, generative AI solutions, retrieval-augmented generation (RAG), and AI agents that enable workflow automation and decision-making. You will advance the adoption of AI-powered development tools and cloud AI services, establish engineering standards for automated evaluation, deployment, and monitoring, and ensure solutions align with security, reliability, and responsible AI practices., You will own people management, talent development, team performance, and delivery across both areas, partnering with product, data, security, and business leaders to shape the roadmap and deliver measurable business value. In this senior leadership role, you will develop engineering leaders, guide critical architecture decisions, and accelerate software modernization and AI adoption through your teams, maintaining technical depth through reviews and targeted prototypes rather than serving primarily as a hands-on individual contributor.

Primary Responsibilities:

• Set engineering strategy, roadmaps and delivery standards. Prioritize the software and AI/ML portfolio with business and product partners, balancing value, risk, technical debt and capacity
• Manage people and lead software and AI/ML teams. Set clear goals, provide regular coaching and feedback, conduct performance reviews, support career development and address performance concerns
• Own hiring, workforce planning, skills development and succession planning. Develop engineering managers and senior engineers, where appropriate, and build a culture of accountability, inclusion and continuous learning
• Own multi-team delivery commitments, resource allocation and engineering budgets. Manage cloud and AI service costs, resolve dependencies and communicate progress, risks and trade-offs to senior stakeholders
• Guide architecture for scalable, resilient and secure cloud-native services, application programming interfaces (APIs), data integrations and reusable platforms. Strengthen quality through design and code reviews
• Lead applied ML, generative AI, retrieval-augmented generation (RAG), AI agents and cloud AI services for workflow automation and decision support. Select approaches based on business needs, data readiness and measurable benefits
• Set evaluation standards for large language models (LLMs) and other models. Require baselines and tests for accuracy, unsupported claims, robustness, bias, latency and cost before release and throughout production use
• Establish ML operations (MLOps) and LLM operations (LLMOps) practices for versioning, automated evaluation, deployment, monitoring and rollback. Oversee model serving, data quality and changes in model performance over time
• Make responsible AI, security and privacy part of design and delivery. Partner with data and security teams on access controls, sensitive-data protection, human review and safeguards against prompt injection and unsafe agent actions
• Strengthen coding, testing and continuous integration and delivery (CI/CD) practices. Own production readiness, service reliability and incident improvements, with clear accountability across software and AI/ML teams
• Drive adoption of reusable components, shared AI capabilities and AI-powered development tools. Measure business impact, user adoption, quality and productivity, and use evidence to scale, improve or stop initiatives
• 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, engineering or a related field, or equivalent practical experience
• 18+ years of relevant engineering experience, including substantial experience delivering production software and cloud-based systems
• 7+ years of people leadership experience, including direct people management, hiring, performance management and career development; experience leading multi-team delivery and developing technical leaders
• 5+ years leading production AI/ML initiatives, with accountability for model evaluation, deployment, monitoring and business outcomes. Experience requirements may overlap and are not additive
• Experience setting engineering roadmaps, managing capacity and costs, and making portfolio trade-offs across competing business and technical priorities
• Proven solid technical grounding in backend engineering, distributed systems, APIs, cloud architecture and secure delivery; experience with a production backend language and Python sufficient to guide technical decisions and reviews
• Proven ability to influence senior stakeholders and partner across product, data, security and operations. Ability to communicate technical risks clearly and lead teams through delivery and organizational change

Preferred Qualifications:

• Experience leading engineering in healthcare or other regulated environments, including responsible use of sensitive data
• Experience developing engineering managers or leading through managers, and building shared software or AI platforms used by multiple teams
• Experience with generative AI, RAG evaluation, vector search, AI agents and ML frameworks; ability to assess when AI is appropriate and when a simpler software solution is better
• Experience with containers, infrastructure as code, model registries and automated model delivery; success scaling AI adoption with demonstrated improvements in quality, productivity or cost

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.