The Company
The Opportunity
Workforce AI at McAfee — the platform, the engineers who build on it, and the adoption program that gets it used — currently runs as several people's side project, split across other jobs. This role consolidates that into a single accountable leader: a Director who owns all four Workforce AI functions, reports directly to the VP of TechOps & Cloud Services, and is judged on adoption outcomes and value — not on how much gets deployed.
You will run a team spanning agent platform engineering and pillar solution engineering, own the adoption and enablement program that closes the gap between people having access to AI tools and actually using them daily, and hold the interface into governance, risk, and FinOps partners who sit outside your org but whose work you depend on. The design principle is thin centre, thick edge: centralize what breaks when it's inconsistent — the gateway, the registry, the permission model — and federate what breaks when it's far from the work — use-case discovery, workflow redesign, local champions.
This is a highly technical, hands-on role. You are expected to go deep enough with your own engineers to review architecture decisions on the agent platform, read an MCP server integration or an evaluation harness, and make a defensible build/buy/wait call — not only manage a roadmap that others design.
Reporting To: Paul Tichener, VP technology services | Service Management
Location:
Frisco, Texas (Hybrid)
Responsibilities:
Agent platform & integration — the rails
Own the platform that makes agents governable, connectable, and measurable: the MCP gateway and internal registry, the LLM gateway, Agent Studio platform engineering, and the software factory that builds and ships agents and internal tooling.
Own the Skills pipeline and scanning policy, and the agent permission model — the scoped access control that lets agents reach only what a task needs.
Own the evaluation harness and agent performance review process that catches quality and security problems before a human opens the diff.
Set one non-negotiable standard here: this layer breaks everything downstream when it's inconsistent, so it is centralized and it is yours to run, not to delegate to a side-of-desk owner.
Stay technical enough to make the hard build/buy/wait calls yourself — gateway architecture, standards adoption, vendor evaluation — and to review your engineers' designs rather than only their status updates.
Pillar solution engineering — the technical funnel
Run the model that takes a capability gap to prototype to production: a named engineer pairs with each business track owner, builds the reference agent or the hard integration, then hands off and enables local builders — success is measured in handoffs completed, not agents owned.
Decide and defend which business pillars get a dedicated engineer — candidates include Content & Creative, Employee Efficiency, Product Creation Velocity, Legal Services, and HR Services — and revisit that call as capability gaps and business value shift.
Keep the center thin: the engineer's job on every pillar is explicitly to make themselves unnecessary on that workflow, not to become its permanent owner.
Partner with federated tracks — Product Creative Velocity, External Customer Success, Data & Analytics, AI Operations — where the engineering ownership sits with the business but the pattern, tooling, and standards come from your team.
Adoption, enablement & value — the function that earns the budget
Own the instrumented adoption scorecard — tracking habitual (weekly) usage against deployment (monthly access), not just seats assigned — and make closing that gap a named, measured priority rather than an assumed side effect of rollout.
Run manager activation as its own KPI: managers who visibly model AI use move adoption more than any other lever the evidence supports, and this program should own that behavior deliberately.
Build and run the champion network, role-based training curricula, and protected time for learning, and facilitate workflow redesign directly with the teams whose processes are changing — this cannot be done to people remotely.
Own capability-map custody: keep a single, current view of where the gaps are, by business track, so pillar and hiring decisions are made against one source of truth.
Run a quarterly cadence of high-impact GenAI pilots, each with a named business sponsor, defined success metrics, and a clear scale/integrate/stop decision — and report value monthly on a scorecard covering where AI is applied, adoption versus target, and value at scale.
Governance, risk & FinOps — own the interface
This function is federated — the people sit in Security, Legal, and Finance, and it is not yours to staff — but the interface into it is yours to own.
Partner with Security on the agent and skill security review pipeline, and on separation-of-duties controls between the people who build agents and the people who run the infrastructure they run on.
Own tool-sanctioning decisions in partnership with Security and Legal, and represent Workforce AI at the AI Guild or SteerCo exception path when a use case needs one.
Partner with Finance on showback and chargeback mechanics and on AI FinOps generally — license optimization, consumption monitoring, and cost governance — so the program has a defensible cost story, not just a capability story.
Lead the team
Build and lead the Workforce AI organization — platform engineers, pillar/solution engineers, and the adoption and enablement team — as their single accountable manager.
Be the person other executives and functions go to for a decision on workforce AI, replacing the current arrangement where ownership is split across other people's other jobs.
Stay hands-on as the team grows: review architecture and agent designs personally, pair with engineers on the hardest integrations, and keep your own technical judgment sharp enough that your team trusts your calls.
The Candidate
Skills:
Strong command of AI/ML ecosystems, AI gateways, agent orchestration, and the responsible-AI governance model required to scale them, in partnership with Security, Legal, and Privacy.
Demonstrated ability to influence executive stakeholders and drive enterprise-wide change, while remaining credible enough with engineers to review their architecture and code directly.Excellent written and verbal communication; highly organized and comfortable managing multiple priorities in a fast-paced, still-being-defined environment.
Preferred Qualifications & Technical Expertise:
Deep, hands-on technical background in AI and agent platforms — you have personally built or operated LLM gateways, agent orchestration, or MCP-based integrations, not only directed people who did.
Track record building adoption scorecards and value-realization frameworks for executive audiences, and comfortable being measured on habitual usage, not just deployment.
Bachelor's degree in Computer Science, Engineering, or a related field; advanced degree a plus.
Experience:
Typically 10+ years of progressive technology experience, including direct leadership of engineers or technical managers, with recent, hands-on experience in generative AI or agentic systems specifically.
Demonstrated experience developing enterprise AI operating models, governance frameworks, and platform strategies that balance innovation, risk management, and business value realization.
Experience with AI FinOps — license optimization, consumption monitoring, showback or chargeback, and cost governance — in partnership with Finance and Procurement.
Experience running a forward-deployed engineering model: pairing technical staff with business owners, shipping a first version, and handing off deliberately so a small central team stays thin.
