Senior Clinical Solutions Manager at Philips driving strategic medical plans and developing clinical information for cardiac monitoring products. Collaborating with cross - functional teams to enhance product compliance and safety initiatives.
About the role
- Senior Field Solutions Architect at CDW designing AI and HPC architectures for customers. Leading presales engagements to provide effective technology solutions in advanced computing.
Responsibilities
- Lead discovery to translate business outcomes into technical requirements and reference architectures for AI/HPC platforms (GPU clusters, distributed training, model serving, orchestration).
- Design scalable compute using leading GPU technologies; specify low-latency fabrics (InfiniBand, RoCE) and high-performance storage (e.g., NVMe, parallel file systems).
- Define security, resilience, data-management, and cost-optimization patterns; document assumptions and trade-offs.
- Build solution designs, sizing guidance, diagrams, and BOMs with clear scope boundaries and delivery alignment.
- Support proposals, statements of work, and risk identification; ensure feasibility with delivery leadership.
- Present technical content to diverse audiences (engineers to executives) and handle deep-dive Q&A.
- Act as a trusted advisor on workload placement, performance tuning considerations, and modernization paths.
- Partner with account managers to qualify opportunities and position the right mix of products, professional services, and managed services.
- Coordinate architecture validation with key vendors; stay current on roadmaps affecting AI/HPC platform choices.
- Collaborate with professional services and project management so designs are deployable and milestones are clear.
- Contribute reusable assets (templates, calculators, patterns) and improve architecture quality through peer reviews.
- Advocate for accurate opportunity data and case hygiene to support forecasting and delivery planning.
Requirements
- Hands-on presales architecture experience designing AI/HPC infrastructure: GPU-accelerated compute, distributed training, model serving, high-performance storage, and low-latency networking.
- Strong consulting fundamentals: discovery, requirements translation, executive-ready communication, and decision framing.
- Proficiency creating architecture diagrams, BOMs, sizing, and cost/comparison analyses.
- Demonstrated collaboration across delivery, program/project management, managed services, and vendor partners.
- Ability to simplify complexity, build stakeholder confidence, and drive consensus.
- Familiarity with orchestration and scheduling (Kubernetes, Slurm, PBS); parallel file systems (Lustre, BeeGFS); object storage; and benchmarking/performance methods.
- Exposure to MLOps/ModelOps pipelines, inference serving, RAG infrastructure, or data-platform integration.
- Industry knowledge in research, public sector, or enterprise analytics use cases.
Benefits
- Health insurance
- Professional development opportunities
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