Technical Program Manager – AI

We are looking for an experienced technical program delivery lead to drive one or two cross-squad initiatives within our AI program. You will take end-to-end delivery accountability for a defined set of initiatives that span multiple AI squads and shared capabilities, covering initiative roadmap alignment, dependency management, risk identification, and execution discipline within that scope.This is a contract engagement on a fast-moving AI program, so you will join initiatives already in flight and be expected to add value quickly. Within your initiatives, you will work across Product, Engineering, Data, and Platform to turn strategy into well-sequenced plans, surface risks before they become delays, and support on-time delivery of outcomes. You will work just as closely with AI Enablement stakeholders and the teams adopting these AI capabilities, so what gets built lands with its intended users. You will also connect your initiatives into the wider program, coordinating with other delivery leads where scope, dependencies, or shared capabilities overlap. The work spans traditional software delivery and agentic AI capabilities, and will need enough fluency in both to partner credibly with technical teams, without acting as an engineer or product owner.What You'll DoInitiative Planning and Roadmap Alignment:Build and maintain an integrated delivery plan for your initiatives across the contributing AI squads and shared capabilities.Align squad-level roadmaps to initiative outcomes, quarterly goals, and platform dependencies, keeping that plan consistent with broader program priorities.Support sequencing, capacity discussions, and dependency trade-offs within your initiatives, escalating conflicts that reach beyond your scope.Delivery ExecutionApply practical understanding of the software development lifecycle to anticipate delivery risks and sequencing constraints.Partner with squad leads on execution discipline, realistic planning, and follow-through across design, build, test, and release.Risk and Dependency ManagementIdentify delivery risks, dependency risks, and execution gaps across the squads contributing to your initiatives.Maintain a current initiative-level risk register with mitigation plans, owners, and escalation paths, and roll material risks up to program leadership.Surface emerging risks early and partner with leaders to resolve, re-sequence, or re-plan.Drive issue resolution within your initiatives by coordinating owners, timelines, and decision points across teams.Cross-Functional Coordination, Enablement, and AdoptionAct as the coordination point for your initiatives across Product, Engineering, Data Science, Platform, Operations, and AI Enablement teams.Manage your initiatives' dependencies on shared AI capabilities such as data platforms, semantic layers, AI tooling, and governance.Make sure interdependencies and handoffs are clearly documented and actively managed, both inside your initiatives and at the seams with other program workstreams.Partner with AI Enablement stakeholders on rollout sequencing, readiness, training, and support needs, so delivery milestones and enablement activity stay in step.Maintain a working feedback loop with adopting teams, routing real usage signals, friction, and unmet needs back to Product and squad leads, who own prioritization.Visibility and Decision SupportProvide clear, decision-ready insights on the delivery health, risks, and dependencies of your initiatives for stakeholders, program leadership, and AI Enablement partners.Surface key initiative data that supports prioritization and trade-off decisions, and into plain-language updates that adopting teams can act on.Maintain a living-view, maintained and current of what is on track, at risk, or blocked within your scope.AI and Agentic Delivery FluencyPlan and coordinate delivery of agentic capabilities within your initiatives, where scope spans model behavior, agent skills and tool definitions, orchestration, and evaluation, rather than conventional feature work alone.Bring enough fluency in agentic architecture, including agent skills, Model Context Protocol (MCP) and similar tool and context standards, and agent-to-agent (A2A) coordination patterns, to sequence work and challenge assumptions credibly.Account for the delivery realities specific to agentic systems, including evaluation and benchmarking cycles, guardrails and human-in-the-loop review, prompt and skill versioning, and non-deterministic output that complicates test and release planning.Track your initiatives' dependencies on shared AI infrastructure such as model endpoints, retrieval and context layers, agent registries, and observability tooling.Use AI and agentic tooling to augment building the delivery visibility mechanism itself, from drafting plans to synthesizing status and monitoring risk, modeling the productivity gains the program exists to deliver.Key Responsibilities:What Success Looks Like:In the first few months, you will have established a single sequenced delivery plan for your initiatives, a live risk and dependency register that leaders actually use, and a reporting rhythm that gives stakeholders and program leadership a reliable read on delivery health. Over the course of the engagement, success looks like milestones tracked against committed dates, agentic capabilities moving through evaluation and release gates without last-minute surprises, adopting teams ready and supported at each release rather than after it, clean coordination with the other initiatives you touch, and documentation clear enough that the work transfers cleanly when the engagement ends.What You'll Bring:Six or more years in technical program delivery, program management, or delivery coordination within software-based teams.A track record of ramping quickly in unfamiliar enterprise environments and delivering with minimal onboarding support.Working knowledge of the software development lifecycle, including planning, development, testing, and release.Experience in Agile delivery environments such as Scrum or Kanban, coordinating delivery across multiple squads contributing to a shared initiative.Familiarity with core product management concepts such as roadmaps, backlogs, prioritization, and trade-off discussions.Strong organizational skills and the ability to manage dependencies, risks, and timelines within a defined scope without day-to-day direction.Hands-on experience using AI tools in your daily work to improve productivity, decision-making, and delivery outcomes, with a clear point of view on where AI and AI agents can strengthen program execution, coordination, and risk management.Working fluency in agentic AI concepts and components, including agent skills, Model Context Protocol (MCP) and comparable tool and context standards, agent-to-agent (A2A) coordination, orchestration frameworks, retrieval and context management, and evaluation practices. You do not need to build these systems, but you should be able to follow the architecture, ask sharp questions, and plan around their real constraints.An understanding of what makes AI delivery different from traditional software delivery, including evaluation-driven release gates, model and prompt versioning, guardrails and responsible AI review, and planning around non-deterministic behavior.Clear communication with both technical and non-technical partners, comfort operating in ambiguity, and sound judgment about what to resolve within your initiatives and what to escalate.Experience partnering with enablement, adoption, or business stakeholders so that delivered capabilities are actually taken up, not just shipped.Bachelor's degree or equivalent experience.Nice to Have:Experience supporting AI, data, or platform-based initiatives.Exposure to enterprise technology environments with shared services or enablement teams.Prior contract or consulting work partnering closely with Product Managers and Engineering Leads on complex, cross-team initiatives.Delivery experience on agentic or LLM-based products, such as multi-agent systems, tool-calling architectures, or AI copilots and assistants.Exposure to AI evaluation, observability, or responsible AI governance practices in an enterprise setting.Experience supporting adoption, enablement, or change management for new technology capabilities in a large organization.How You'll Work:You are a detail-oriented executor with a bias toward clarity and follow-through. You bring enough technical and product fluency to ask sharp questions and anticipate delivery challenges from day one. You spot risks early and raise them rather than waiting to see how they play out. You keep the people who will use these capabilities in view, not just the teams building them. You are comfortable owning a defined slice of a larger program, staying tightly coordinated with the leads around you rather than working in isolation. You help teams stay aligned without adding overhead, and you document as you go, so the work holds up after you hand it off.