Job description

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together. We're seeking a skilled Lead Software Engineer to help build intelligent automation, predictive analytics, and agentic AI capabilities for IT Operations. In this role, you will design, build, and help operationalize machine learning models and AI-driven workflows that improve observability, speed up incident response, and support self-healing infrastructure across the enterprise. You'll work closely with senior engineers, architects, and data scientists to bring these capabilities into production. 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 • AI/ML Development: Design, build, and maintain machine learning models and AI-driven automation for IT operations use cases, including predictive analytics, intelligent automation, and support for autonomous remediation • Data & Platform Engineering: Build and maintain data pipelines that ingest structured and unstructured data from observability and ITSM platforms (e.g., Splunk, Dynatrace, ServiceNow, logs, metrics, traces) • Feature Engineering & Model Support: Develop and maintain feature sets that support anomaly detection, predictive alerting, capacity forecasting, and operational intelligence use cases • Model Operationalization: Partner with data scientists and platform teams to deploy and productionize ML models, and help ensure they perform reliably at scale • AI-Driven Automation: Integrate ML and AI outputs into orchestration and automation platforms (e.g., Jenkins, Interlink) to support closed-loop, self-healing operational workflows • Agentic AI Development: Build components of agentic AI solutions that reason, plan, and execute actions across IT operations, including incident triage, root cause analysis, and resolution • MLOps Practices: Follow established MLOps standards, model lifecycle practices, and data governance requirements aligned with enterprise security, privacy, and compliance policies • Cloud & Infrastructure: Use cloud native and hybrid architectures to support real-time analytics, scalable model inference, and resilient AI platforms • Cross-Functional Collaboration: Work with engineering, operations, security, and business stakeholders to understand requirements, document your work, and contribute reusable patterns for AI-driven operations • Responsible AI: Apply ethical AI principles, including fairness, transparency, explainability, and accountability, throughout the development lifecycle 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 in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience • 3+ years of experience building and deploying AI/ML solutions in production environments • 3+ years of experience in Python and SQL, with hands-on experience using distributed data processing frameworks such as Spark • 2+ years of experience applying ML to IT operations, infrastructure, reliability engineering, or observability domains • 2+ years of experience with cloud platforms (Azure, AWS, or GCP), including hybrid and cloud native architectures • 2+ years of experience with MLOps practices, CI/CD pipelines, model monitoring, and lifecycle management • 1+ years of experience with NLP, Large Language Models (LLMs), Generative AI, and modern AI frameworks • 1+ years of experience with containerization and orchestration technologies such as Docker and Kubernetes Preferred Qualifications: • Hands-on experience contributing to agentic or autonomous AI systems in operational contexts • Experience in healthcare, regulated industries, or large-scale enterprise platforms • Experience working in agile, product-driven environments delivering scalable, high-impact solutions • Working knowledge of IT operations concepts, including incident management, CMDB, monitoring, alerting, and reliability engineering • Familiarity with Responsible AI and governance frameworks, including fairness, transparency, and explainability • Solid technical communication skills, including the ability to explain AI concepts to non-technical stakeholders *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.

Requirements

Must-haves

AI/ML Development: Design, build, and maintain machine learning models and AI-driven automation for IT operations use cases, including predictive analytics, intelligent automation, and support for autonomous remediation
Data & Platform Engineering: Build and maintain data pipelines that ingest structured and unstructured data from observability and ITSM platforms (e.g., Splunk, Dynatrace, ServiceNow, logs, metrics, traces)
Feature Engineering & Model Support: Develop and maintain feature sets that support anomaly detection, predictive alerting, capacity forecasting, and operational intelligence use cases
Model Operationalization: Partner with data scientists and platform teams to deploy and productionize ML models, and help ensure they perform reliably at scale
AI-Driven Automation: Integrate ML and AI outputs into orchestration and automation platforms (e.g., Jenkins, Interlink) to support closed-loop, self-healing operational workflows
Agentic AI Development: Build components of agentic AI solutions that reason, plan, and execute actions across IT operations, including incident triage, root cause analysis, and resolution
MLOps Practices: Follow established MLOps standards, model lifecycle practices, and data governance requirements aligned with enterprise security, privacy, and compliance policies
Cloud & Infrastructure: Use cloud native and hybrid architectures to support real-time analytics, scalable model inference, and resilient AI platforms
Cross-Functional Collaboration: Work with engineering, operations, security, and business stakeholders to understand requirements, document your work, and contribute reusable patterns for AI-driven operations
Responsible AI: Apply ethical AI principles, including fairness, transparency, explainability, and accountability, throughout the development lifecycle
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience
3+ years of experience building and deploying AI/ML solutions in production environments
3+ years of experience in Python and SQL, with hands-on experience using distributed data processing frameworks such as Spark
2+ years of experience applying ML to IT operations, infrastructure, reliability engineering, or observability domains
2+ years of experience with cloud platforms (Azure, AWS, or GCP), including hybrid and cloud native architectures
2+ years of experience with MLOps practices, CI/CD pipelines, model monitoring, and lifecycle management
1+ years of experience with NLP, Large Language Models (LLMs), Generative AI, and modern AI frameworks
1+ years of experience with containerization and orchestration technologies such as Docker and Kubernetes

Nice-to-haves

Hands-on experience contributing to agentic or autonomous AI systems in operational contexts
Experience in healthcare, regulated industries, or large-scale enterprise platforms
Experience working in agile, product-driven environments delivering scalable, high-impact solutions
Working knowledge of IT operations concepts, including incident management, CMDB, monitoring, alerting, and reliability engineering
Familiarity with Responsible AI and governance frameworks, including fairness, transparency, and explainability
Solid technical communication skills, including the ability to explain AI concepts to non-technical stakeholders

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonsqlazureawsgcpci/cddockerkubernetesagile

Platform: career-feeds
Effort: medium

Benefits

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401k