Senior Manager, Software Engineering - AI, Data & Digital Platforms - Remote

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 are seeking a highly experienced Senior Software Engineering Manager to lead the strategy, architecture, development, and delivery of next-generation AI-powered digital platforms, enterprise applications, and data products. This leader will be responsible for building and scaling high-performing engineering teams that deliver modern cloud-native applications, AI/ML solutions, intelligent automation, and enterprise data platforms.

The ideal candidate combines deep technical expertise with solid people leadership, driving engineering excellence, innovation, modernization, and AI adoption across the organization while aligning technology investments with business outcomes.

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:

Leadership & Engineering Management

• Lead and develop multidisciplinary engineering teams across software development, digital engineering, data engineering, AI/ML engineering, and platform engineering
• Establish technical vision, engineering standards, architecture governance, and execution strategies for enterprise-scale applications and AI-enabled platforms
• Drive engineering excellence through best practices in software development, DevOps, testing automation, observability, reliability, and operational readiness
• Partner with executive leadership to define technology roadmaps, investment priorities, and long-term modernization strategies
• Build and mentor high-performing engineering organizations, fostering innovation, accountability, and continuous learning
• Manage engineering capacity planning, talent development, performance management, and organizational growth

Application Development & Modernization

• Lead modernization of legacy platforms into cloud-native, API-first, event-driven, and AI-enabled architectures
• Define technology standards and reference architectures for enterprise application development
• Oversee development and deployment of scalable web, mobile, digital, and backend platforms supporting business growth
• Partner with architecture, infrastructure, security, and operations teams to ensure scalable, resilient, and cost-effective solutions
• Drive adoption of microservices, containerization, cloud services, and modern software engineering practices
• Lead the teams focusing on leveraging enterprise AI and data transformation initiatives that improve customer experience, operational efficiency, and business decision-making
• Oversee the design and implementation of scalable digital, data platforms

Data, Analytics & AI Platforms

• Guide the design of enterprise data lakehouse and distributed computing architectures
• Oversee development and deployment of AI/ML solutions including:
  • Fraud Detection & Prevention
  • Lending Underwriting & Risk Scoring
  • Customer Personalization & Recommendations
  • Predictive Analytics & Forecasting
  • Natural Language Processing (NLP)
  • Intelligent Process Automation
• Lead initiatives involving:
  • AI Agents and Autonomous Workflows
  • Retrieval-Augmented Generation (RAG)
  • Vector Databases
  • Enterprise Knowledge Management
  • Intelligent Document Processing
  • Conversational AI Platforms

AI-Enabled Engineering Productivity

• Drive adoption of AI-assisted software engineering tools including GitHub Copilot, Claude Code, and similar developer productivity platforms
• Establish engineering practices leveraging AI to improve:
• Developer productivity
• Code quality
• Test automation
• Documentation generation
• Code reviews
• Delivery velocity
• Develop organizational standards, training programs, and governance frameworks for responsible use of AI development tools

Cross-Functional Leadership

• Collaborate with Product Management, Business Leaders, Enterprise Architecture, Security, and Operatio…