Senior Manager - Delivery - Applied AI
Direct Hire
Remote Local to MN
Non-Negotiables:
- Local to MN
- People Management (team of 7-10)
- Taking AI end to end into production
- AWS
Top Skills:
- Former Engineer that is a technical people leader
Nice to have Skills:
- Supported salesforce and/or snowflake platforms
Role Overview:
CoE: 5 year strategic mission/plan starting in 2023 to impact more lives from 6-7k patients in a year to more than 10k+ in a year using AI to scale the business in a rational and productive way to scale their services and overall outreach (# of lives they can save in a year), started deploying ML last year, now treating AI as a strategic technology, brining consistency with AI transform the whole business.
Job Description:
Senior Manager, Delivery Applied AI
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 thier technology portfolio. This highly technical leadership role is responsible for leading 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. This individual will lead AI engineering organization, establish engineering best practices, drive end-to-end AI solution delivery, and partner closely with the Senior Director of AI to scale the organization's AI capabilities.
This role is responsible for ensuring AI initiatives move from concept to production with speed, quality, and operational excellence while building and developing a high-performing, multidisciplinary AI team.
ACCOUNTABILITIES: (The primary functions, scope, and responsibilities of the role)
Technical Leadership & AI Delivery
- 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 successfully 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.
Leadership & People Management
- Builds a culture of technical excellence, accountability, collaboration, innovation, knowledge sharing, continuous learning, quality, engagement, and operational ownership.
- Provides leadership and oversight for internal staff and contract resources, including performance calibration, development planning, and corrective action in partnership with HR and IT leadership.
- Communicates 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.
- Establishes and executes talent strategies, including hiring, onboarding, coaching, career development, performance expectations, succession planning, and retention of top AI talent.
Operational Excellence
- Own engineering execution metrics including delivery predictability, quality, velocity, operational stability, and production reliability.
- Drive continuous improvement across engineering practices, including but not limited to 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.
Strategic Partnership
- 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.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical field (Master's preferred).
- 10+ years of software engineering experience with progressively increasing technical leadership responsibilities.
- 5+ years leading AI/ML engineering organizations delivering production AI solutions.
- Proven experience building, deploying, and operating machine learning, generative AI, and AI-powered applications in production.
- Deep understanding of the complete AI Development Lifecycle (AIDLC), including experimentation, model development, evaluation, deployment, MLOps, monitoring, governance, and lifecycle management.
- Strong software engineering background with expertise in cloud-native architectures, APIs, distributed systems, CI/CD, DevOps, and modern engineering practices.
- Demonstrated experience leading cross-functional technology initiatives involving software engineering, infrastructure, cloud operations, data engineering, security, architecture, product, and business stakeholders.
- Proven ability to build, mentor, and scale high-performing AI engineering organizations while consistently delivering complex enterprise initiatives.
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