• 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

• Formulate benchmarking methods for high-performance computing and AI tasks. • Perform and bring these methods to completion on large-scale GPU clusters. • Assess performance metrics to detect optimization opportunities and upgrade architecture. • Develop and maintain telemetry infrastructure to capture performance data. • Collaborate closely with hardware engineering, software development, and customer-facing teams to define performance requirements, fix bottlenecks, and validate configurations against real-world workloads. • Deploy and manage observability stacks including monitoring tools like Prometheus, visualization platforms such as Grafana, NVIDIA's DCGM, and custom telemetry solutions to provide actionable insights into cluster health, utilization, and performance trends. • Work directly with engineering and collaborate with internal partners to understand their performance requirements, conduct on-site benchmarking engagements, and deliver detailed analysis and recommendations for workload optimization. • Maintain extensive knowledge of industry-standard benchmarks in advanced computing and machine learning fields such as HPL, HPCG, MLPerf, and NCCL tests. • Contribute to developing new benchmarking methodologies for emerging workloads.

• Clinical Expertise: Contribute to the development and refinement of annotation protocols and clinical guidelines for different use cases. • Identify edge cases, ambiguities, and potential sources of bias in clinical data and model behavior. • Participate in applied clinical AI research, including hypothesis development and evaluation of model performance. • Perform reviews of nuclear medicine imaging exams and provide clinical insight and annotations to support AI model training and refinement. • Validate AI outputs for clinical accuracy, safety, and relevance across defined use cases. • Provide concise, actionable clinical feedback on product features and workflows. • Collaborate asynchronously with clinical, product, and data science teams.

Role Description Servico is seeking an AI lead to join our growing team in Belgium! The AI Lead is responsible for identifying, building, validating, and scaling AI-powered product solutions that drive real customer and business outcomes. This role is hands-on and AI-first. An AI Lead does not operate through documents, tickets, or handoffs. They build working solutions using AI, engage directly with customers, and iterate rapidly based on real-world feedback. The AI Lead does everything an AI Product Engineer does - plus owns customer discovery, validation, prioritization, and measurable impact. Core Responsibilities - AI-First Product Building - Build working product solutions using AI as the primary execution medium - Rapidly prototype, iterate, and refine solutions to validate ideas early - Use AI agents for requirements analysis, design exploration, implementation, and testing - Move from problem understanding to working solution without reliance on legacy SDLC rituals - Customer Discovery & Engagement - Engage directly with customers to understand workflows, pain points, and unmet needs - Use live demos and working prototypes to drive customer conversations - Validate solutions through real usage, not assumptions or opinions - Build trust by clearly explaining AI behavior, limitations, and tradeoffs - Problem Framing & Opportunity Identification - Frame problems clearly and precisely before execution - Identify new AI-powered solution opportunities and revenue potential - Evaluate ideas based on impact, feasibility, and learning velocity - Use experimentation to discard weak ideas quickly - Outcome Ownership - Define success in terms of adoption, retention, efficiency, and revenue impact - Prioritize initiatives based on outcomes, not feature volume - Continuously assess whether solutions are creating real value - Own the full lifecycle from idea to impact - Cross-Functional Leadership - Work closely with AI Product Engineers and AI Developers within AI Pods - Align engineering, design, and data efforts around outcomes - Communicate clearly with internal stakeholders - Represent the product externally with credibility and clarity What This Role Is Not - Not a backlog or roadmap-only role - Not a handoff-based product manager - Not a requirements writer detached from execution Qualifications - Strong ability to build working software using AI tools - Proven experience rapidly prototyping and iterating on product ideas - Deep understanding of probabilistic AI systems and guardrails - Ability to reason about systems end-to-end - Experience engaging directly with customers - Ability to identify business and revenue opportunities - Strong product judgment and taste - Comfort prioritizing under uncertainty - Ability to explain complex AI behavior clearly and honestly - Strong storytelling grounded in real system behavior - Comfortable leading through influence rather than authority