Own and drive engineering productivity lift targets, measured through cycle time, code velocity, and defect reduction
Lead AI-driven cost reduction initiatives across IT and support functions
Conduct a full audit and rationalization of Rackspace's AI licensing footprint, eliminating duplicative programs and spend
Report quarterly on quantified productivity gains with results tied directly to compensation milestones
Define and launch 3–5 standardized AI sales plays segmented by vertical and workload within the first 6 months
Translate Rackspace's private cloud and AI runtime capabilities into packaged go-to-market motions
Embed AI positioning into key partner relationships including VMware, Palantir, Uniphore, and Rubrik
Equip the Sales organization with clear AI messaging, competitive framing, and active deal support
Establish tracking infrastructure for AI-influenced pipeline and revenue attribution
Monitor model releases, inference economics, developer ecosystem shifts, and AI governance developments on a continuous basis
Translate market shifts into concrete strategy adjustments within 30–60 days of identification
Advise the CEO, marketing leadership, and Board on AI competitive positioning and narrative
Drive AI messaging alignment across earnings communications, analyst relations, and partner narratives
Deliver a formal competitive brief to the CEO and Board on a quarterly cadence
Lead a lean AI Labs function with a bias toward speed and commercial relevance
Incubate 1–2 focused, defensible IP assets per year aligned to GTM priorities
Apply rigorous ROI discipline — kill low-return experiments quickly and reallocate resources
Ensure Labs output directly supports customer-facing revenue strategy, not standalone research
Requirements
Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field required
Advanced degree (Master's or PhD) in AI, Machine Learning, Computer Science, or related field strongly preferred
15+ years of progressive technology leadership, with at least 7 years in senior roles operating at the intersection of AI, cloud infrastructure, and commercial strategy
Demonstrated track record of translating AI initiatives into quantifiable business outcomes — revenue generated, costs reduced, efficiency delivered
Experience operating in or alongside managed services, cloud, or enterprise infrastructure businesses strongly preferred
Prior P&L ownership or accountability for a business unit, product line, or cost center
Deep fluency in the current AI landscape: foundation models, inference infrastructure, agent frameworks, AI governance, and enterprise deployment patterns
Ability to operate simultaneously as strategist, operator, and commercial leader — without losing effectiveness in any mode
Strong executive communication skills; able to advise a Board, align a sales force, and challenge an engineering team in the same week
Financial discipline: comfortable building business cases, owning targets, and reporting results with rigor
Partner ecosystem fluency, particularly with hyperscalers, infrastructure ISVs, and AI platform vendors
Comfortable making decisions with incomplete information in a fast-moving market
Earns cross-functional trust quickly; can lead without direct control
Holds themselves and their team to high standards; ties effort to outcomes relentlessly