Principal Software Engineer (AI Platform & Full Stack Engineering) - Hybrid in MN

UnitedHealth Group•Minnetonka, MN

14h•$134,600 - $230,800•Hybrid

About The Position

As a Principal Software Engineer, you will serve as a senior individual contributor responsible for designing and delivering AI-driven, full stack platform capabilities that enable enterprise-scale intelligence, optimization, and decisioning. You will combine deep hands-on engineering with architectural leadership, defining standards for AI/ML systems, platform design, and modern software development while enabling scalable, secure, and high-performing applications. This position follows a hybrid schedule with four in-office days per week.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field
10+ years of experience in software engineering with solid full stack development expertise
6+ years of experience designing microservices and distributed systems
5+ years of experience with CI/CD, containerization (Docker), and DevOps practices
4+ years of experience with cloud platforms (AWS preferred)
Solid experience with Java, Python, Node.js, and modern frontend frameworks (React/Angular, TypeScript, REST, GraphQL)
Experience building AI-enabled applications and platform services
Experience integrating AI/ML or Generative AI capabilities (e.g., APIs, embeddings, decisioning features)
Experience delivering complex, multi-team engineering initiatives
Experience using AI-assisted development tools to accelerate delivery

Nice To Haves

Experience with AI/ML platforms, LLM-based applications, or agentic systems
Experience building enterprise-scale platforms for analytics, optimization, or decision intelligence
Proven knowledge of responsible AI, model lifecycle, and governance frameworks
Experience in regulated industries (healthcare, financial services, etc.)

Responsibilities

Define scalable architecture and engineering patterns for AI-powered applications, APIs, and platform services
Establish guardrails for open-source usage, AI models, and data governance
Lead design decisions across frontend, backend, and cloud layers ensuring performance, scalability, and reliability
Design and integrate AI/ML and Generative AI capabilities into production applications
Review and guide usage of AI models and datasets ensuring compliance with licensing, regulatory, and enterprise standards
Enable responsible AI practices across application development
Design and build end-to-end applications (UI, APIs, services, data layers)
Develop scalable microservices, domain APIs, and distributed systems using modern frameworks
Implement cloud-native and event-driven architectures
Drive engineering excellence through CI/CD pipelines, containerization, and automation
Build reusable platform components, services, and developer tooling to improve scalability and speed
Ensure system reliability including observability, performance tuning, and resiliency
Leverage AI-assisted development tools (coding agents, LLMs) to improve engineering productivity
Build intelligent tooling and workflows to streamline development and governance processes
Provide technical leadership, code reviews, and design guidance across teams
Collaborate with product, architecture, and data science teams to deliver integrated solutions
Drive adoption of best practices, reusable patterns, and modern engineering standards