System Architect-AI platform

System Architect-AI platform Job Description Job Summary As the Hardware Architect for Philips Multi-Modal Imaging AI General Management Platform, you will lead end-to-end hardware architecture design for our unified edge and on-prem AI platform. This platform runs on SUSE Linux Micro + K3s, supporting containerized AI workloads (TensorRT/ONNX/NVIDIA acceleration) and virtualized SaMD applications via KubeVirt/KVM.

Responsibilities

• You will collaborate closely with software architects, clinical product teams, regulatory QA, and global engineering to define hardware specifications for multi-modal medical devices including CT, MR, DSA, and Ultrasound.
• You will balance clinical performance, medical regulatory compliance, air-gapped hospital deployment constraints, power and thermal limits, and long-term platform scalability to support cardiovascular, neuro, stroke, pulmonary, and structural heart AI workflows.
• Key Responsibilities 1.
• Develop and maintain the overall hardware reference architecture for the multi-modal medical imaging AI platform, covering edge on-device deployments integrated with DSA/CT/MR/ultrasound and hospital on-prem server clusters.
• 2.
• Co-design with software architects to align hardware capabilities with K3s, KubeVirt virtualization, GPU acceleration, distributed monitoring (OpenTelemetry), air-gapped software packaging (Zarf), and secure remote access (FRP).
• Optimize resource partitioning between containerized AI workloads and VM-based SaMD workloads.
• 3.
• Define compute, GPU, memory, storage, and network specifications for medical-grade hardware; conduct vendor evaluation, benchmarking, and performance validation for NVIDIA accelerators, local high-speed storage, and low-latency IO for medical DICOM image streams.
• 4.
• Address medical environment constraints including air-gapped deployment, EMI/EMC requirements, thermal management, 24/7 clinical continuous operation, and hospital power resilience.
• 5.
• Support medical regulatory alignment with NMPA, IEC 62304, and IEC 60601.
• Collaborate with QA and regulatory teams to document hardware architecture, risk analysis, and hardware change control for SaMD and AI medical applications.
• 6.
• Lead hardware performance benchmarking for multi-modality image reconstruction, AI inference latency, and concurrent workload testing for clinical AI pipelines such as FFR, surgical planning, and lesion assessment.
• 7.
• Evaluate hardware supply chain options, define reference BOMs, and design the hardware upgrade roadmap for the 2026-2028 platform rollout timeline covering DSA/CT pilot, EI product expansion, and mass clinical rollout.
• 8.
• Collaborate with global Philips hardware teams and local clinical customers to collect site requirements and resolve deployment hardware issues during prototype validation and clinical pilot phases.
• Create hardware architecture documentation, reference design whitepapers, and architecture review materials for internal technical reviews and executive presentations.
• 5+ years of professional experience as a hardware architect or senior hardware system engineer, with proven experience designing server or edge computing hardware for on-prem or edge AI workloads.
• Deep knowledge of x86 hardware architecture, PCIe, GPU architecture (NVIDIA preferred), high-speed storage (NVMe), and low-latency networking.
• Hands-on experience with virtualization (KVM) and containerized workload hardware resource planning.
• 5.
• Experience conducting system-level performance benchmarking, thermal and power budget analysis, and hardware sizing for parallel inference workloads.
• Working knowledge of IT infrastructure requirements in hospital environments; experience with air-gapped, secure medical system deployment is a strong plus.
• Awareness of medical device standards including IEC 60601, IEC 62304, and NMPA medical software and SaMD compliance requirements.
• 3.
• Experience designing hybrid edge-server hardware architectures supporting DICOM multi-modal imaging pipelines.

Requirements

• Required Qualifications & Experience 1.
• Bachelor's or Master's degree in Computer Engineering, Electronic Engineering, Hardware Systems, or related disciplines.
• Practical understanding of Linux-based systems (SUSE Linux preferred).
• Familiarity with hardware resource allocation for mixed workloads, including virtual machine workloads alongside GPU-accelerated container AI workloads.
• Fluent written and spoken English, capable of communicating with global cross-functional teams.
• Preferred Qualifications 1.
• Familiarity with Kubernetes/K3s, KubeVirt, and GPU virtualization resource scheduling concepts such as vGPU and Mdev.
• Experience in hardware specification definition for long-lifecycle industrial or medical platforms with 7-10 year support cycles.

Preferred

• Previous working experience in the medical device or medical imaging industry.
• Experience with CT, MR, DSA, or ultrasound systems is highly preferred.

Benefits

• Opportunity to architect a flagship unified AI platform for Philips multi-modality clinical imaging products. • Cross-border collaboration with global Philips medical technology teams. • Clear roadmap spanning prototype development, clinical pilot, and nationwide market rollout. • Competitive compensation, flexible working policies, complete medical industry training, and regulatory development resources.