- Craft and communicate the overarching architecture vision, solution intent, and technical strategies, clearly articulating trade offs and securing buy in across product, engineering, and enterprise stakeholders.
- Develop comprehensive architectural plans and reference architectures that guide the implementation of integrated systems aligned with business goals and consumer journeys.
- Collaborate with business, product, and enterprise architects to understand strategy and translate it into actionable technical guidance for engineering teams, helping resolve complex technical issues and ensuring adherence to architectural standards.
- Provide recommendations and technical guidance to improve scalability, performance, reliability, security, and reusability within budget, resource, and business constraints
- Ensure that solution architectures for both internally built and acquired capabilities conform to enterprise architecture standards, security policies, cloud best practices, and compliance requirements.
- Engage with multiple delivery teams and vendors, offering hands on architectural support through design reviews, architecture walkthroughs, and evaluation of emerging technologies.
- Communicate architectural decisions, risks, and trade offs effectively to a broad range of stakeholders, including senior leadership, in a clear and executive ready manner.
- Maintain and evolve architectural artifacts including solution intents, architectural diagrams, standards, patterns, and documentation to ensure consistency, clarity, and reuse.
- Present solution options, decision matrices, and clear recommendations with rationale to drive alignment and informed decision making.
- Partner with delivery teams to identify, prioritize, and mitigate technical debt, balancing short term delivery needs with long term platform health.
- Continuously enhance technical knowledge and apply innovative solutions using emerging technologies, including AI enabled and cloud native approaches.
- Promote the use of shared services, reusable frameworks, and federated architecture patterns across engineering teams.
- Guide engineering teams to deliver secure, resilient solutions by embedding non functional requirements and enterprise guardrails early in the design process.
- Apply an AI first mindset, enabling teams to adopt AI based and agentic approaches where they provide measurable value.
- Operate as a self motivated, self directed architecture partner who proactively identifies gaps, risks, and opportunities across initiatives.
- Demonstrate strong collaboration skills by building trust based relationships across product, engineering, TPM, security, and enterprise architecture teams.
- 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
- MBA
- Bachelor's degree in engineering, computer science, data science, or a related field of study
- 8+ years of experience in a product organization or product-related role with direct client or customer-facing responsibilities
- 3+ years of hands-on experience building and shipping AI or ML-powered products - not AI-adjacent roles; candidates must have directly owned AI product outcomes
- 3+ years of AI/ML product leadership experience in healthcare, health tech, or enterprise SaaS
- Experience designing experiments (A/B tests, champion/challenger models) to validate AI product hypotheses before committing to full build
- Experience in complex matrix organizations, multi-disciplinary teams, and cross-functional program
- Experience with generative AI product development, including prompt engineering, RAG architectures, evaluation frameworks, and responsible deployment practices
- Deep, working knowledge of AI/machine learning concepts including supervised and unsupervised learning, NLP, generative AI, model evaluation, and MLOps
- Hands-on familiarity with AI tooling and platforms
- Keen understanding of the healthcare and technology marketplace, including competitors and emerging trends in automation, AI, and ML adoption for payer and provider solutions
- Proven understanding of ethical AI principles - fairness, bias detection, explainability, and regulatory considerations (e.g., HIPAA in healthcare contexts)
- Demonstrated ability to manage multiple concurrent AI product initiatives in a fast-paced environment
- Proven ability to communicate AI product strategy and complex technical tradeoffs to executive and commercial audiences
- Demonstrated ability to partner with data science and engineering teams to define data requirements, evaluate model outputs, and make quality/accuracy tradeoffs
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.
