Job Summary
The Senior AI Technical Project Manager leads end-to-end execution of complex artificial intelligence initiatives across a matrixed enterprise environment. The role owns delivery planning, integrated timelines, dependency management, stakeholder communications, delivery governance, and execution transparency. It partners closely with AI/Data Science practice leads, AI Architecture, Engineering, Product, QA/Responsible AI, and Enterprise AI portfolio leadership to move solutions from concept through production readiness and business adoption. This role succeeds through influence, technical fluency, and disciplined orchestration—not through direct authority over discipline staffing or ownership of architecture, model design, or product decisions. Role Mandate Create a single, credible delivery plan across AI, data science, architecture, engineering, testing, product, and business workstreams. Establish operating cadence, decision forums, quality checkpoints, escalation paths, and portfolio-level visibility. Identify and resolve cross-team risks and dependencies early while ensuring accountable leaders make the decisions reserved for their disciplines. Drive consistent AI delivery practices and standards across the portfolio in partnership with functional and portfolio leaders. Key Responsibilities Delivery Execution Own integrated planning, milestones, release timelines, critical path, assumptions, and dependency tracking across multiple pods or workstreams. Lead delivery ceremonies, working sessions, decision reviews, and executive status communications; maintain accurate RAID and action logs. Coordinate cross-pod capacity needs and surface constraints; partner with discipline leaders who retain resource-allocation authority. Maintain delivery momentum by clarifying ownership, sequencing work, driving follow-through, and escalating unresolved blockers. Test, Quality, and Production Readiness Orchestrate test governance and lifecycle integration from design through deployment, in partnership with Engineering and the Testing Center of Excellence. Facilitate definition and adoption of measurable quality-gate criteria for “production ready,” including functional, technical, operational, data, model, security, and Responsible AI considerations. Coordinate defect taxonomy, severity standards, triage cadence, remediation ownership, and release-impact de
Key Responsibilities
Required Qualifications
- Bachelor’s degree in computer science, engineering, information systems, business, or a related field; equivalent experience considered.
- 7+ years of technical project/program management experience delivering complex, cross-functional technology initiatives, including 2+ years supporting AI, machine learning, data science, or generative AI solutions.
- Demonstrated success managing multi-team delivery in a matrixed organization without relying on direct reporting authority.
- Strong command of Agile, hybrid, and product-oriented delivery practices, including release planning, dependency management, RAID management, and executive reporting.
- Working knowledge of the AI solution lifecycle, including data readiness, experimentation, model evaluation, integration, testing, deployment, monitoring, and Responsible AI controls.
- Excellent facilitation, negotiation, conflict-resolution, written communication, and executive presentation skills.
Preferred Qualifications
- Experience delivering generative AI, large language model, RAG, conversational AI, recommendation, computer vision, or predictive analytics solutions.
- Familiarity with cloud AI platforms, APIs, data platforms, MLOps/LLMOps, CI/CD, observability, security, privacy, and model governance.
- PMP, PgMP, PMI-ACP, SAFe, Scrum, or comparable delivery certification.
- Experience in retail, consumer, digital commerce, loyalty, marketing technology, or other high-volume customer-facing environments.
Core Competencies
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Enterprise delivery leadership |
AI/ML technical fluency |
Influence without authority |
Executive communication |
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Risk and dependency management |
Quality and release governance |
Structured problem solving |
Continuous improvement |
Success Measures
- AI initiatives deliver against committed outcomes with improved predictability, transparency, and decision velocity.
- Cross-pod dependencies, capacity constraints, and delivery risks are identified early and actively managed.
- Quality gates, UAT, and production-readiness reviews occur consistently with clear evidence and accountable approvals.
- Executives and stakeholders receive concise, accurate, action-oriented reporting and escalation support.
- AI delivery standards are adopted across teams and show measurable improvement in cycle time, quality, and operational readiness.
Leadership Profile
The successful candidate is a calm, credible operator who can translate between business outcomes and technical execution. They bring structure to ambiguity, challenge assumptions respectfully, distinguish coordination from decision authority, and create the conditions for AI specialists and product teams to deliver high-quality outcomes together.
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Role design note: This position owns delivery orchestration and governance. Architecture, engineering, testing, product acceptance, and portfolio authority remain with the designated accountable leaders. |