Lead AI ML Engineer - Remote

UnitedHealth Group•Eden Prairie, MN

20h•$145,500 - $249,500•Remote

About The Position

The Lead AI/ML Engineer is a strategic and hands on technical leader responsible for designing, building, and deploying advanced AI and machine learning solutions across the full application stack. This role blends deep expertise in AI/ML, cloud native architectures, and full stack software engineering with cross functional leadership, driving innovation, operational excellence, and scalable AI adoption across the organization. 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.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field
9+ years of professional software engineering experience, with demonstrated success designing and delivering high quality, commercial full stack applications in production environments
6+ years of hands on proficiency in Java, Python, SQL, and Linux shell scripting, with production experience in distributed systems
5+ years of AI/ML engineering experience, including deploying models at scale and leading or mentoring technical teams
4+ years of Solid cloud experience in various CSPs (especially AWS)
2+ years of applied AI/ML experience, including deep learning, NLP/NLU, semantic understanding, intent classification, ASR, predictive modeling, and time series analysis
2+ years of experience in MongoDB and MLOps
Proven cloud experience with Google Cloud (Vertex AI) and working knowledge of AWS, including cloud native architectures, containerization, and orchestration
Hands on experience with Hadoop ecosystems, and MLOps practices for model lifecycle management
Experience integrating AI/ML models into end to end, user facing applications
Solid understanding of API based architectures, distributed data systems, and scalable backend services
Must be authorized to work in the United States without the need for current or future employer-sponsored visa sponsorship (e.g., H-1B, TN, F-1/OPT, CPT, or other employment-based visa status)

Nice To Haves

Experience working in enterprise or regulated environments with governance and compliance requirements
Solid grounding in statistics, probability theory, optimization, simulation, and data modeling
Ability to clearly present complex analytical and AI concepts to both technical and non technical audiences

Responsibilities

Architect, develop, and deploy scalable AI/ML platforms and full stack application solutions, spanning model training, inference, APIs, and user experiences
Implement AI/ML computing infrastructures and application stacks using frameworks such as AWS Bedrock core/Google ADK, with orchestration using AWS AgentCore for multi agent workflows
Design end to end pipelines covering data ingestion, feature processing, model serving, and observability
Build and maintain production grade backend services and APIs using Java and Python
Develop and integrate frontend and lightweight UI components (eg, dashboards, internal tools) to operationalize AI workflows and analytics
Apply modern software engineering practices including CI/CD pipelines, containerization, and cloud native deployment
Partner closely with data scientists, product managers, UX teams, and business stakeholders to translate complex business requirements into scalable AI and full stack solutions
Act as a bridge between research, engineering, and product teams to ensure solutions are practical, performant, and maintainable
Define and document architecture roadmaps, reference architectures, and standard operating procedures for AI/ML systems
Ensure alignment with enterprise standards for reliability, security, fairness, transparency, and regulatory compliance
Lead technical design reviews, guide architectural decisions, and oversee model evaluation, optimization, and deployment cycles
Mentor junior and senior engineers, setting clear expectations for code quality, system design, and operational readiness
Conduct applied research to advance capabilities in Generative AI, NLP, NLU, computer vision, and predictive modeling
Stay current with emerging tools, architectures, and industry trends, and translate them into actionable engineering practices
Lead disaster recovery and business continuity planning for AI and application infrastructure
Monitor, tune, and continuously improve deployed models and services for performance, accuracy, scalability, and cost efficiency