Intelligent Solutions Delivery Lead

McKesson • Work at Home - Tennessee, USA (WTNA), TN

3d • Remote

About The Position

Lead delivery of AI and intelligent automation solutions across prioritized use cases, owning execution, technical quality, architecture integrity, release readiness, delivery scope, timelines, success criteria, risks, dependencies, and technical trade-offs to ensure successful outcomes. Translate business problems into AI designs, user stories, technical specifications, and IT design specifications using systems thinking, workflow analysis, user journey analysis, and structured problem decomposition. Lead iterative prototyping, pilot development, and rapid experimentation to validate solution hypotheses, while ensuring every pilot is designed with a clear and practical path to scalable, maintainable, enterprise-grade production deployment. Provide solution architecture leadership across AI and agent-based patterns, including retrieval-augmented generation, orchestration, copilots, integrations, data flows, and cloud or platform alignment to ensure solutions are scalable, secure, compliant, interoperable, and maintainable. Guide teams in building production-ready AI and automation solutions by supporting API integrations, data pipelines, transformations, workflow orchestration, deployment practices, and engineering standards for performance, reliability, and monitoring. Ensure required EARB and AIRB approvals are obtained and validate solution quality by reviewing test cases, confirming control adherence, supporting governance and compliance requirements, and establishing appropriate operational monitoring. Partner with product, architecture, security, data governance, engineering, and operations teams to align requirements, maintain delivery transparency, manage dependencies, and support structured transition to production support and ongoing operations. Responsible for key deliverables, including technical specifications, AI solution designs, user stories, IT Design Specification, test case reviews, EARB and AIRB submissions, architecture documentation, implementation artifacts, operational guides, and knowledge transfer materials.

Requirements

• Bachelor's Degree
• At least 10 years of experience in technology delivery and solution implementation
• Generative AI, large language models, machine learning, and agentic architecture patterns such as retrieval-augmented generation, orchestration, and copilots.
• Intelligent automation, enterprise integration patterns
• AI governance, security and compliance frameworks
• Enterprise architecture principles, and production delivery practices in regulated or controlled environments.
• Strong solution architecture capability across AI, data, cloud, and enterprise systems, with the ability to translate business problems into implementable solution designs, user stories, and technical specifications.
• Proven delivery leadership, prototyping and pilot-to-production execution, stakeholder communication, governance coordination, test case review, control validation.
• Strong engineering oversight for APIs, data pipelines, deployment, monitoring, and operational readiness.
• Ability to operate across problem framing, solution design, architecture leadership, governance, and end-to-end delivery execution in ambiguous environments.
• Ability to balance rapid experimentation with enterprise-grade rigor, influence matrixed teams, drive clarity and accountability, and ensure solutions are scalable, secure, maintainable, and ready for production support.

Responsibilities

• Lead delivery of AI and intelligent automation solutions across prioritized use cases, owning execution, technical quality, architecture integrity, release readiness, delivery scope, timelines, success criteria, risks, dependencies, and technical trade-offs to ensure successful outcomes.
• Translate business problems into AI designs, user stories, technical specifications, and IT design specifications using systems thinking, workflow analysis, user journey analysis, and structured problem decomposition.
• Lead iterative prototyping, pilot development, and rapid experimentation to validate solution hypotheses, while ensuring every pilot is designed with a clear and practical path to scalable, maintainable, enterprise-grade production deployment.
• Provide solution architecture leadership across AI and agent-based patterns, including retrieval-augmented generation, orchestration, copilots, integrations, data flows, and cloud or platform alignment to ensure solutions are scalable, secure, compliant, interoperable, and maintainable.
• Guide teams in building production-ready AI and automation solutions by supporting API integrations, data pipelines, transformations, workflow orchestration, deployment practices, and engineering standards for performance, reliability, and monitoring.
• Ensure required EARB and AIRB approvals are obtained and validate solution quality by reviewing test cases, confirming control adherence, supporting governance and compliance requirements, and establishing appropriate operational monitoring.
• Partner with product, architecture, security, data governance, engineering, and operations teams to align requirements, maintain delivery transparency, manage dependencies, and support structured transition to production support and ongoing operations.
• Responsible for key deliverables, including technical specifications, AI solution designs, user stories, IT Design Specification, test case reviews, EARB and AIRB submissions, architecture documentation, implementation artifacts, operational guides, and knowledge transfer materials.

Benefits

• Comprehensive benefits to support physical, mental, and financial well-being
• Competitive compensation package
• Annual bonus or long-term incentive opportunities may be offered

What This Job Offers

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