[Hiring] Sovereign AI Enterprise Architect @Hewlett Packard Enterprise

Role Description

We are seeking a Sovereign AI Enterprise Architect to support strategic UKIMEA customers in designing, deploying, and scaling secure, high‑performance AI platforms. This role sits at the intersection of AI infrastructure, HPC, Kubernetes, and enterprise architecture, helping sovereign and regulated organisations build resilient, compliant AI environments.

You will act as a trusted technical advisor, working closely with customers, partners, and internal teams to architect end‑to‑end AI solutions—from bare metal and GPUs through to orchestration, operations, and governance.

Key Responsibilities

• Design and architect sovereign AI platforms for enterprise and public sector customers
• Lead end‑to‑end AI infrastructure deployments, from design through implementation
• Advise on Kubernetes-based AI platforms, GPU clusters, and HPC integrations
• Partner with stakeholders to translate business requirements into scalable architectures
• Support solution validation, performance tuning, and operational readiness
• Provide technical leadership across customer engagements and advisory projects

Qualifications

• Strong, demonstrated experience deploying and configuring enterprise Kubernetes platforms, including:
  • Rancher RKE2
  • Red Hat OpenShift
  • CNCF-compliant Kubernetes
• 5+ years hands-on Linux experience (RHEL, Ubuntu)
• Strong background in Ansible automation frameworks
• Experience integrating platforms using REST APIs
• Familiarity with SLURM on Kubernetes frameworks (e.g. Slinky, SUNK)
• Strong understanding of distributed systems architecture (GPU clusters, multi-node training)
• Knowledge of HPC architectures, network topologies, and high-performance storage
• Experience with GPU and accelerator platforms (NVIDIA, AMD, or custom ASICs)
• Familiarity with CUDA, NCCL, and distributed training optimisation
• Knowledge of NVIDIA AI Enterprise tooling, including BCM and DCGM

Requirements

• High-performance storage (NVMe, parallel file systems, object storage)
• Data centre infrastructure (power, cooling, racks, redundancy)
• Advanced networking (InfiniBand, RoCE, RDMA, 100–800GbE fabrics)
• Virtualisation and containerisation (Docker, Kubernetes, OpenShift)
• Infrastructure as Code: Terraform, Ansible, Pulumi
• AI training and inference pipelines
• Model lifecycle management and MLOps platforms
• Data pipeline orchestration (Airflow, Kubeflow)
• Performance benchmarking and workload profiling
• Large-scale model deployment (on‑prem, edge, hybrid cloud)
• Cloud & Hybrid AI services across AWS, Azure, and GCP
• Hybrid cloud design and migration strategies
• Secure connectivity between on‑prem AI systems and cloud environments
• Cost optimisation for large-scale compute workloads
• Zero-trust architecture principles
• Identity and Access Management (IAM)
• Data governance and privacy controls
• Secure multi‑tenant AI platforms
• Regulatory compliance (e.g. ISO, SOC 2, GDPR)
• Observability and monitoring (Prometheus, Grafana, ELK)
• SLA/SLO design for AI workloads
• Capacity planning for GPU environments
• Incident management and root cause analysis
• High availability, fault tolerance, and disaster recovery planning

Benefits

• Health & Wellbeing: Comprehensive suite of benefits that supports physical, financial, and emotional wellbeing.
• Personal & Professional Development: Programs to help you reach career goals, whether you want to become a knowledge expert or apply your skills to another division.
• Unconditional Inclusion: A culture that celebrates individual uniqueness and values varied backgrounds.