Technical Project Manager- AI

Enterprise AI Delivery & Governance- Technical Project Manager Contract Bolingbrook, IL Position 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. 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 decisions. Plan and manage UAT and business sign-off; ensure the Product Owner or designated business authority makes the final acceptance decision. Delivery Governance Run portfolio-level quality reviews and ensure required evidence, risks, exceptions, and decisions are visible to Enterprise AI leadership. Coordinate cross-pod quality-gate approvals while preserving technical approval authority with Engineering and/or Architecture leaders. Route cross-pod conflicts to the appropriate portfolio or functional leader, document decisions, and track resolution to closure. Define, socialize, and continuously improve AI delivery practices, templates, controls, metrics, and standards across the portfolio. Decision Rights and Accountability Boundaries The Senior AI Technical Project Manager is accountable for orchestration and delivery outcomes. The role coordinates decisions but does not replace the accountable technical, product, or portfolio authority. Plan and timeline: Integrated plan, cadence, dependencies, risks, reporting — Functional leads validate estimates and commitments Cross-pod capacity: Demand view, constraints, sequencing, escalation — AI/DS, Architecture, Engineering, QA, and Product leaders allocate resources Testing and defects: Governance, lifecycle integration, triage process, tracking — QA/Test CoE and Engineering own testing methods and technical disposition Production readiness: Facilitation of criteria, evidence, exceptions, and readiness review — Engineering, Architecture, Product, Security, and Responsible AI approve within their domains UAT and sign-off: Plan, coordination, evidence, issue closure — Product Owner or designated business authority accepts the solution Cross-pod conflict: Issue framing, options, routing, decision log, follow-through — Enterprise AI portfolio and functional leadership decide when authority is required AI delivery standards: Process design, adoption, measurement, continuous improvement — Portfolio and discipline leaders approve standards affecting their domains 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. 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 Enterprise delivery leadership AI/ML technical fluency Influence without authority Executive communication 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. Role design note: This position owns delivery orchestration and governance. Architecture, engineering, testing, product acceptance, and portfolio authority remain with the designated accountable leaders. #J-18808-Ljbffr Prairie