Requisition Number: 2352352 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 diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. The Enterprise Information Security (EIS) team is responsible for cybersecurity across our organization. We support our business and members by reducing risk, rapidly responding to threats, focusing on business resiliency and securing new acquisitions. The Principal AI / Machine Learning Data Engineer role focuses on designing and building scalable data platforms that enable advanced analytics, machine learning, and AI-driven solutions. This role will support the development of intelligent systems that process large-scale event and operational data, enabling faster insights, automation, and decision-making across the organization. This position sits at the intersection of data engineering, machine learning, and AI, with an emphasis on building modern data pipelines and enabling production-grade AI capabilities. Ideal Candidate Profile: - Demonstrated experience building and operating production data platforms and pipelines across batch and streaming workloads - Solid hands-on engineering in Python and SQL; familiarity with JVM languages (Java/Scala) in Spark ecosystems is a plus - Experience with distributed processing and lakehouse/warehouse patterns (eg, Spark/PySpark, Databricks, Snowflake) - Experience building ingestion frameworks for structured and unstructured data, including event/log and semi-structured formats - Experience enabling Generative AI solutions in production (eg, RAG-style architectures), including retrieval patterns and evaluation/monitoring practices - Familiarity with knowledge-centric data approaches (eg, metadata-driven systems, entity resolution, and/or graph concepts) to improve discoverability and downstream analytics - Solid data quality, observability, and monitoring mindset (profiling, validation, alerting, and reliability improvements) - Comfort with orchestration, CI/CD, containerization, and infrastructure-as-code (eg, Airflow, GitHub Actions, Docker, Terraform, Kubernetes) - Cloud experience (AWS, Azure, and/or GCP), including secure handling of sensitive data (PII/PHI) and collaboration with compliance partners - Ability to lead through influence, mentor engineers, and translate ambiguous problems into scalable technical roadmaps You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week. Primary Responsibilities: - Design, develop, and maintain scalable data pipelines and data platforms supporting analytics, machine learning, and AI use cases - Build and optimize ingestion frameworks for large-scale structured and unstructured data, including streaming and event-driven sources - Partner with cross-functional stakeholders to understand evolving data and AI needs and define long-term technical solutions - Enable and support machine learning and AI workflows, including feature engineering, data preparation, and model deployment support - Drive strategic initiatives around Generative AI, data quality, observability, lineage, and governance - Develop and maintain frameworks that support rapid experimentation and deployment of AI/ML solutions - Introduce and evolve best practices in data modeling, orchestration, testing, and monitoring - Identify and champion opportunities for platform scalability, performance optimization, and cost efficiency - Collaborate with product, analytics, and infrastructure teams to deliver high-impact data and AI solutions - Build and maintain reusable parsing, enrichment, analytic, and service libraries to accelerate delivery across teams - Work comfortably under time-sensitive conditions while ensuring thoroughness - Maintain high ethical standards and the ability to remain objective and confidential You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in. Required Qualifications: - Bachelor's degree or equivalent experience - 5+ years of experience designing, building, and operating production data pipelines and platforms - 5+ years of hands-on development with Python (preferred) and/or Java, including code reviews, packaging, and deployment - 5+ years of experience with Spark (PySpark) and Databricks (or similar distributed data processing platform) - 2+ years of experience leveraging and deploying Generative AI use cases to production environments - Solid SQL skills and experience working with data lakes and warehouses (e.g., Databricks, Snowflake) - Experience building ingestion frameworks for structured and unstructured data (e.g., event/log, semi-structured JSON), including parsing and enrichment patterns - Experience designing and scaling ELT/ETL frameworks with orchestration tools such as Airflow (or equivalent) - Experience implementing data quality, observability, and monitoring practices (e.g., data quality checks, pipeline SLAs/SLOs, alerting) - Experience with metadata, lineage, and governance concepts and tooling (e.g., data catalogs, lineage, access controls) - Experience with data modeling best practices for analytics and ML use cases - Experience with DevOps and CI/CD practices and tools (e.g., GitHub Actions), containerization, and infrastructure-as-code (e.g., Docker, Kubernetes, Terraform) - Experience supporting ML/AI workflows (feature engineering, data preparation, and model deployment enablement); exposure to MLOps practices is a plus - Demonstrated ability to partner with cross-functional stakeholders, translate requirements into technical solutions, and lead through influence Preferred Qualifications: - Experience with cloud platforms such as AWS, Azure, or Google Cloud, including managed data services - Experience with streaming and event-driven architectures (e.g., Kafka, Kinesis, Event Hubs) - Experience with data quality and validation frameworks (e.g., Great Expectations, Deequ) and/or data observability tooling - Experience enabling MLOps practices (e.g., feature stores, model registries, experiment tracking, deployment automation) - Experience with lakehouse architectures, Delta Lake, and advanced Spark optimization/performance tuning - Experience with data visualization tools and libraries such as Plotly, seaborn, and Chartjs - Experience with machine learning and predictive analytics - Familiarity with security and privacy concepts for data platforms (e.g., least privilege, PII/PHI handling) and working with compliance partners *All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $112,700 to $193,200 annually based on full-time employment. We comply with all minimum wage laws as applicable. Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants. 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. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment. #BI-Hybrid

• Architect and deploy LLM and agentic AI systems in production • Lead ML solution design across customer engagements • Mentor engineers and influence technical direction • Ensure scalable, maintainable, enterprise-grade ML systems

• Build and improve agentic AI workflows for processing technical engineering documentation • Develop Computer Vision pipelines for diagram and symbol detection • Design and maintain Knowledge Graph structures representing system components and relationships • Develop multi-agent orchestration pipelines using modern LLM frameworks • Integrate retrieval, reasoning, and tool-calling capabilities into AI systems • Build APIs and backend services supporting AI workflows and experimentation • Collaborate closely with engineers and researchers in a fast-paced R&D environment

• Be responsible for the architecture of hybrid game and machine learning systems. • Design, develop and maintain learning and non-learning systems. • Assist programming and machine learning teams throughout development phases. • Spread machine learning knowledge across the organization. • Improve the function of your team.