AI Platform Adoption & Enablement Lead

U.S. Bank • Minneapolis, MN

1d • $170,255 - $200,300 • Hybrid

About The Position

The AI Platform Adoption & Enablement Lead is a senior individual contributor responsible for accelerating enterprise adoption of AI by guiding how AI applications are designed, built, deployed, and monitored. This role provides deep technical expertise across the AI lifecycle, with a strong focus on application development patterns, observability, and operational excellence. This leader partners across engineering, product, platform, and business teams to ensure AI use cases are implemented using scalable, secure, and enterprise-aligned approaches. They play a critical role in defining best practices, enabling teams to build responsibly, and ensuring AI systems are measurable, reliable, and continuously improving in production.

Requirements

• Masters degree or equivalent work experience
• Strong hands-on experience delivering AI projects in production, including engineering, deployment, and monitoring of AI use cases
• Deep familiarity with Microsoft Azure AI suite and AWS AI offerings, with Azure as the primary platform
• Exposure to a wide range of AI platforms, tools, and models, including custom modeling and execution
• Experience with AI application delivery and use case development, not just machine learning or data science
• Ability to review and provide technical feasibility guidance for AI requests across the enterprise
• Leadership and people management skills, ideally with experience managing developers and data scientists
• Experience with agentic AI use cases and tools (e.g., LangChain, LangGraph,AI Foundry, Amazon Bedrock, 3rd party agentic capabilities thru vendors in CRM, ERP & other spaces), with deep knowledge to guide what will be more suitable for solving business problems using gen AI models, human agents, and AI agents and suggest strong technical design to promote reuse and scale for value
• Familiarity with enterprise-scale AI enablement, including documentation, education, and self-serve approaches

Responsibilities

• Provide hands-on guidance on how to design, build, and deploy AI applications across a variety of use cases (ML, GenAI, agentic AI)
• Define and operationalize observability and monitoring frameworks for AI systems, including performance, drift, reliability, and usage tracking
• Guide teams in implementing production-ready AI solutions, ensuring scalability, resiliency, and compliance with enterprise standards
• Partner with product and engineering teams to shape and refine AI use cases, balancing feasibility, value, and technical complexity
• Drive adoption of enterprise AI platforms, tools, and services through practical enablement and technical advisory
• Establish and document best practices, patterns, and reusable approaches for AI application development and deployment
• Support teams in evaluating and selecting appropriate models, architectures, and tools based on use case requirements
• Ensure AI implementations align with enterprise expectations for security, risk, and governance
• Provide expert guidance on building AI applications end-to-end, including prompt design, orchestration, model integration, and API-based deployment
• Advise on architectural patterns for different categories of AI solutions (predictive ML, GenAI, agent-based systems)
• Partner with teams to translate business problems into scalable AI solutions
• Provide leadership and oversight for enterprise MLOps practices, including CI/CD, model registry, and automated rollback.
• Ensure AI systems meet regulatory, security, and privacy standards in collaboration with risk and compliance stakeholders.
• Define and review SLAs, KPIs, and observability standards for AI services, ensuring operational excellence and accountability.
• Set the architectural vision for a scalable, modular AI ecosystem spanning data ingestion, feature stores, training infrastructure, and inference.
• Champion standards for model governance, including versioning, data lineage, explainability, and auditability.
• Evaluate emerging AI technologies and approaches, and define adoption roadmaps aligned with business value and risk tolerance.
• Own the strategic evolution of the on‑prem AI platform stack (e.g., Airflow, Elasticsearch) and its operating model.
• Ensure delivery of self‑service platforms, APIs, and tooling that enable teams to innovate efficiently and safely.
• Partner with cloud, DevOps, and security leaders to balance performance, cost efficiency, scalability, and compliance.
• Define and standardize observability frameworks for AI applications
• Establish metrics for model performance, latency, cost, reliability, drift, and user interaction quality
• Guide implementation of monitoring tools and feedback loops for continuous improvement
• Ensure production AI systems are measurable, traceable, and auditable
• Act as a trusted advisor for teams evaluating and developing AI use cases
• Provide technical feasibility guidance and identify risks, trade-offs, and dependencies early
• Drive consistency in how use cases are implemented across the enterprise

Benefits

• Healthcare (medical, dental, vision)
• Basic term and optional term life insurance
• Short-term and long-term disability
• Pregnancy disability and parental leave
• 401(k) and employer-funded retirement plan
• Paid vacation (from two to five weeks depending on salary grade and tenure)
• Up to 11 paid holiday opportunities
• Adoption assistance
• Sick and Safe Leave accruals of one hour for every 30 worked, up to 80 hours per calendar year unless otherwise provided by law

What This Job Offers

• Job Type: Full-time
• Career Level: Senior