Principal Technical Program Manager

F5•Seattle, WA

2d•$193,600 - $290,400

About The Position

At F5, we strive to bring a better digital world to life. Our teams empower organizations across the globe to create, secure, and run applications that enhance how we experience our evolving digital world. We are passionate about cybersecurity, from protecting consumers from fraud to enabling companies to focus on innovation. Everything we do centers around people. That means we obsess over how to make the lives of our customers, and their customers, better. And it means we prioritize a diverse F5 community where each individual can thrive. Senior Technical Program Manager — AI Program Orchestration About the Role We are seeking a seasoned Senior Technical Project Manager to orchestrate our enterprise AI program end to end. This is not a task-tracking role — it is a full-orchestration role. You will design and stand up the program's operating model, own governance and decision rights across interlocking workstreams, drive a global AI agent adoption rollout, and lead the organization's transition from waterfall-heavy delivery to agile execution. You will operate at the intersection of emerging technology, business strategy, and organizational change, translating executive ambition into a running program rhythm with measurable outcomes. You will be expected to operate independently from day one: defining charters and RACIs where none exist, negotiating dotted-line relationships between programs, establishing reporting and communication infrastructure, and delivering an evidence-based readout of traction — adoption, velocity, and ROI — within the first quarter.

Requirements

10+ years of project/program management experience, including 3+ years in technical program management or program orchestration roles spanning multiple concurrent workstreams; proven ability to stand up governance, operating models, and reporting infrastructure from scratch with minimal oversight.
Program governance expertise: Demonstrated experience authoring charters and RACI models, defining decision rights and escalation paths, and managing dotted-line relationships across programs and executive stakeholders.
Enterprise rollout leadership: Track record designing and executing phased, multi-geography or multi-function technology adoption programs, including wave planning, success criteria definition, and adoption-gap remediation.
Agile transformation experience: Hands-on experience designing sprint operating models and leading teams through waterfall-to-agile transitions — not just operating within an existing agile framework.
AI literacy: Practical, hands-on understanding of core AI concepts, including building autonomous agents, working with models, and establishing guardrails; able to speak credibly with both engineers and executives.
Executive communication: Proven ability to design and run executive-level reporting rhythms, publish program readouts, and facilitate investment and resourcing conversations with senior leadership.
High emotional intelligence (EQ): Exceptional communication and negotiation skills; demonstrated success navigating change resistance, resolving ownership ambiguity between senior stakeholders, and sustaining high-trust relationships across teams.
Business acumen: Demonstrated experience evaluating financial outcomes, proving ROI with quantified metrics (adoption, velocity, business value), and shaping quarterly roadmaps and resourcing asks.
Technical adaptability: Comfort managing rapidly prototyped, custom, or lightly engineered internal applications and integrating them into program operations.
Data-centric mindset: Sharp eye for data quality and hygiene issues specifically as they relate to deploying successful AI business solutions.

Responsibilities

Program Governance & Operating Model Design: Author and publish workstream charters and RACI models from a blank page, defining decision rights, escalation paths, and reporting vs. informing relationships across programs and executive offices (e.g., dotted-line alignment with parallel enterprise AI programs).
Negotiate and formalize role boundaries between overlapping program leads and stakeholders, resolving ambiguity in ownership before it becomes friction.
Establish and run recurring mission-alignment forums with partner programs: cadence, standing agendas, and shared guiding metrics.
Health-check the governance model in flight, confirming that decision rights and cross-program relationships work in practice, and revise as needed.
Global Rollout & Adoption Orchestration: Design and execute phased, global rollout programs for AI capability adoption (e.g., organization-wide agent-building initiatives), including wave planning by geography and function, success criteria, and tiered workstream structures.
Lead adoption at scale: identify and unblock adoption gaps (training, tooling friction, lagging organizations), adjust subsequent wave plans based on wave-one learnings, and capture early wins to fuel momentum.
Drive change management through resistance, guiding stakeholders and teams through the cultural and operational shifts of AI adoption while maintaining high-trust relationships.
Agile Transformation & Delivery Leadership: Design, propose, and secure sign-off on sprint operating models for technical delivery teams — cadence, ceremonies, backlog ownership, and definition of done — and lead teams through waterfall-to-agile transitions.
Run full sprint cycles, facilitate retrospectives, and iteratively tune the delivery model based on team feedback and velocity data.
Build comprehensive work breakdown structures and maintain rigorous, dependency-aware program plans spanning multiple concurrent workstreams.
Communications, Reporting & Program Visibility: Stand up program communication infrastructure as a single source of truth: mission site hubs housing charters, RACIs, rollout schedules, status reports, and FAQs.
Design and operate a bi-weekly executive stakeholder reporting rhythm with consistent templates, distribution, and metrics — starting with a lightweight status cadence from week one.
Maintain shared workboards that make cross-program dependencies, owners, and dates visible in one place.
Use Case Lifecycle & Value Realization: Surface, articulate, and evaluate high-value AI opportunities across the organization; translate business pain points into structured technical use cases.
Own the full lifecycle of AI use cases from discovery through deployment, ensuring each is tied to defined business outcomes and key performance metrics.
Develop robust business cases, facilitate investment conversations with leadership, and publish quarterly readouts covering adoption by org/geo, delivery velocity, and status against the rollout schedule.
Deliver forward-looking roadmaps and resourcing proposals to secure the next quarter's investment.
Data Governance, Tooling & Responsible AI: Identify and drive resolution of data quality and hygiene issues impacting AI use cases.
Coordinate and manage custom-built or lightly engineered (vibe-coded) internal tools and applications, including their integration into program workflows.
Ensure established guardrails for safe and responsible AI deployment are defined, met, and auditable.