Senior Manager Delivery Applied AI

About the role

The Senior Manager – Delivery – Applied AI serves as the single point of accountability for the execution, delivery, and operational success of enterprise AI solutions within the NMDP technology portfolio. This highly technical leadership role leads multidisciplinary AI engineering teams that design, build, deploy, integrate, and support production AI, machine learning, generative AI, and agentic AI solutions that create measurable business value. The ideal candidate combines deep AI engineering expertise with exceptional execution and delivery leadership.

Responsibilities

• Lead the end-to-end execution of enterprise AI initiatives from ideation through production deployment and operational support.
• Drive the delivery of AI, machine learning, generative AI, and agentic AI solutions that create measurable business value.
• Provide strong technical leadership, engineering excellence, and architectural alignment across AI engineering initiatives.
• Establish and continually improve a compliant AI Development Lifecycle (AIDLC), spanning experimentation, model development, evaluation, deployment, MLOps, monitoring, governance, and ongoing optimization.
• Ensure engineering excellence through code quality, responsible AI practices, security, scalability, observability, testing, and operational readiness.
• Partner closely with Enterprise Architecture, Software Engineering, IT Operations, Cloud Engineering, Data Engineering, Security, other technology teams, and Quality and Regulatory Affairs (QRA) to integrate AI capabilities into enterprise platforms and applications.
• Collaborate with Product Management, business stakeholders, and functional leaders to translate business priorities into scalable AI solutions and deliver measurable outcomes.
• Remove delivery roadblocks, proactively manage risks, and ensure predictable execution across multiple concurrent AI initiatives.
• Build a culture of technical excellence, accountability, collaboration, innovation, knowledge sharing, continuous learning, quality, engagement, and operational ownership.
• Provide leadership and oversight for internal staff and contract resources, including performance calibration, development planning, and corrective action in partnership with HR and IT leadership.
• Communicate status, risks, dependencies, and forward-looking needs to IT leadership, the Senior Director of AI, business partners, and other executive stakeholders.
• Lead, mentor, and develop a multidisciplinary AI organization consisting of AI engineers, machine learning engineers, MLOps engineers, AI platform engineers, and other AI technical specialists.
• Manage team capacity, sprint execution, prioritization, staffing, and resource allocation across AI initiatives.
• Recruit, hire, onboard, and retain top AI talent as the organization grows.
• Establish clear career development paths, coaching plans, performance expectations, and technical growth opportunities for team members.
• Foster collaboration across engineering disciplines and promote knowledge sharing and engineering best practices.
• Own engineering execution metrics including delivery predictability, quality, velocity, operational stability, and production reliability.
• Drive continuous improvement across engineering practices, including Agentic Coding, Evals, MLOps, AI platform capabilities, and emerging modern engineering practices.
• Standardize engineering processes, reusable frameworks, documentation, and delivery methodologies.
• Ensure compliance with enterprise security, privacy, responsible AI, and governance standards.
• Serve as the primary execution partner to the Senior Director of AI.
• Translate strategic priorities into executable engineering roadmaps, delivery plans, and staffing strategies.
• Coordinate execution across AI, technology, product, and business organizations to ensure successful delivery of enterprise AI initiatives.
• Help mature and scale the AI organization through improved engineering processes, organizational design, delivery governance, and operational excellence.

Requirements

• Bachelor’s degree in a relevant field.
• Proven experience in machine learning, software engineering, and AI/ML leadership.
• Experience with cloud-native technologies and AWS.
• Strong background in establishing and improving AI Development Lifecycle (AIDLC).
• Experience in leading multidisciplinary AI engineering teams.
• Demonstrated ability to manage and mentor technical teams.
• Healthcare background preferred.

Skills

• Machine Learning
• Applied AI
• Software Engineering
• Management and Leadership
• AI/ML Leadership
• Cloud Native
• Artificial Intelligence

Benefits

Individual compensation offered for this position within the range of $145,000 - $185,000/yr will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors. For more details, refer to the total rewards summary.

Schedule

This is a hybrid position based in Minneapolis, MN. The position is anticipated to close on August 31, 2026.