ph3Job Description /h3 pAs a member of the ACE Value Engineering Outcomes (VEO) team, you will play a key role in ensuring that AI and High Performance Computing (HPC) use cases are successfully translated, deployed, and executed across Roche's AI Factory and HPC infrastructure. Operating at the intersection of business domains, RDT AI teams (such as Applied AI), and platform engineering, you will contribute to owning the end‑to‑end flow from use case intent to real, running workloads, ensuring technical feasibility, scalability, and alignment with platform capabilities. /p h3Use Case Structuring, Challenge Readiness /h3 ul liPartner with business domain teams and RDT AI teams to clarify, structure, and challenge AI and HPC use cases. /li liAssess readiness, dependencies, and feasibility across data, infrastructure, and platform constraints. /li liEnsure use cases are technically viable and aligned with platform capabilities before execution. /li liIdentify gaps early and guide teams toward executable pathways. /li /ul h3Workload Translation, Architecture Platform Routing /h3 ul liTranslate use cases into executable workload designs, including compute, storage, orchestration, and data requirements. /li liDefine how workloads are deployed across AI Factory, HPC, and hybrid environments. /li liLeverage experience with containerized and distributed systems (e.g., Kubernetes, HPC schedulers) to ensure workloads are production‑ready. /li liDevelop reusable patterns to standardize workload deployment and scaling. /li /ul h3Platform Onboarding Execution /h3 ul liDrive onboarding of workloads into platform environments, ensuring all technical prerequisites are met. /li liWork closely with engineering and platform teams to ensure workloads are successfully deployed and running. /li liTroubleshoot and resolve issues across the full stack, from infrastructure to application behavior.



/li liEnsure workloads progress from onboarding to first successful execution. /li /ul h3Governance Integration Execution Pathways /h3 ul liEmbed governance, compliance, and prioritization frameworks into execution pathways, ensuring use cases are not only approved but operationally viable. /li liEnsure governance decisions are reflected in how workloads are structured, routed, and executed. /li liAct as a bridge between governance intent and real‑world platform execution. /li liHelp ensure that governance is defined and consistently applied through real execution practices. /li /ul h3Outcomes, Performance Scaling /h3 ul liEnsure workloads progress to successful execution and measurable outcomes aligned with business needs. /li liIdentify performance, scaling, and reliability challenges in real‑world environments. /li liEstablish feedback loops to inform platform, architecture, and process improvements. /li liContribute to scaling patterns across multiple use cases and domains. /li /ul h3Cross‑Functional Leadership /h3 ul liConnect and align business domain teams, RDT AI teams, platform engineering, and infrastructure teams to enable successful workload execution. /li liInfluence decisions across organizational boundaries to ensure successful delivery. /li liProvide clarity on execution pathways, risks, and constraints. /li liContribute to shaping how the AI Factory ecosystem operates end‑to‑end.



/li /ul h3Performance Optimization /h3 ul liTrack and improve time‑to‑value from use case intake to first successful execution. /li liIdentify cross‑team bottlenecks and optimization opportunities across intake, translation, and execution. /li liContribute to continuous improvement of workflows and operating models. /li /ul h3Qualifications – Education / Experience /h3 ul liBachelor’s degree or advanced degree in Computer Science, Engineering, or a related discipline. /li liStrong experience in AI/ML platforms or HPC environments. /li liHands‑on experience with containerized workloads and orchestration (e.g., Kubernetes, CaaS) and/or HPC scheduling environments. /li liProven ability to take workloads from concept to running systems. /li liComfortable working across infrastructure, platform, and application layers. /li liExperience collaborating with both technical teams and business/domain stakeholders. /li /ul h3Technical Skills /h3 ul liUnderstanding of AI/ML or HPC workload characteristics. /li liExperience with cloud and/or on‑premise compute environments. /li liFamiliarity with orchestration frameworks (Kubernetes, Slurm, etc.). /li liAbility to diagnose and resolve issues in real runtime environments. /li liAbility to connect technical solutions to business outcomes and use case needs. /li liStrong systems thinking and problem‑solving skills. /li /ul h3Leadership Skills /h3 ul liAbility to influence without authority across engineering, AI, and business stakeholders. /li liStrong ownership mindset, driving work through to execution and outcomes. /li liComfortable operating in ambiguity and shaping new ways of working. /li liEnterprise mindset with strong collaboration across organizational boundaries. /li liBias toward action and solving real problems, not just defining them. /li /ul pRoche is an Equal Opportunity Employer. /p /p #J-18808-Ljbffr

📌 Value Engineering and Outcomes Engineer (Madrid)
🏢 Roche
📍 Madrid