AI / ML Solutions Architect

Base pay range $250,000.00/yr - $300,000.00/yr Building World Class Teams - DC / Cloud / HPC / AI / ML - US and Europe Solutions Architect - HPC/AI/ML | Remote | Exciting AI Infrastructure Scale-Up We're working with an innovative client who's at the forefront of AI/ML infrastructure, providing cutting-edge solutions that power large‑scale distributed training and inference workloads. They're looking for an exceptional Solutions Architect to join their growing team and work directly with customers pushing the boundaries of what's possible with AI. The Role This is a truly exciting opportunity to bridge the gap between bleeding‑edge technology and real‑world enterprise applications. You'll be the technical expert who architects and deploys sophisticated Kubernetes environments and high‑performance networking solutions specifically designed for AI/ML and HPC workloads. Day‑to‑Day Responsibilities Designing and implementing Kubernetes environments with high‑performance networking for demanding AI/ML workloads Supporting customers with Slurm‑based workload management to optimize their large‑scale distributed training and inference Creating proof‑of‑concept projects and benchmarking performance to demonstrate value Acting as a trusted technical advisor, understanding customer business needs and developing tailored, scalable solutions Providing deep expertise on GPU acceleration, distributed computing, and AI frameworks Collaborating with product and engineering teams, using customer insights to shape the product roadmap What You'll Bring Essential Technical Skills Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or related field 7+ years' experience as a Solutions Architect, Technical Account Manager, or Cloud Engineer in AI, HPC, or cloud computing Deep expertise in cloud computing concepts and architecture, with practical experience designing scalable infrastructure Strong knowledge of high‑performance networking, particularly InfiniBand fabric architecture and configuration Hands‑on experience with Kubernetes for orchestrating containerized workloads at scale, including custom resource definitions and operators Proven experience with Slurm workload manager for scheduling and managing large‑scale distributed AI/ML training jobs Solid understanding of NVIDIA GPU architectures (A100, H100, etc.) and their optimal configurations for different workload types Practical knowledge of NVIDIA NCCL for multi‑GPU and multi‑node communication optimization Demonstrated ability to design and implement complex, production‑grade infrastructure solutions from the ground up Experience troubleshooting performance bottlenecks in distributed AI/ML systems Highly Desirable Master's or PhD in AI, Machine Learning, High‑Performance Computing, or Cloud Computing Experience with bare metal infrastructure provisioning and configuration for AI workloads Knowledge of containerized AI workflow platforms such as Kubeflow for MLOps pipelines and MLflow for experiment tracking Familiarity with high‑performance storage architectures including Lustre parallel file systems and GPUDirect Storage for eliminating CPU bottlenecks Understanding of popular AI/ML frameworks (PyTorch, TensorFlow, JAX) and their distributed training capabilities Experience with network performance tuning and RDMA protocols Knowledge of container runtimes optimized for GPU workloads What's on Offer Generous equity scheme (2x base salary) Company bonus Comprehensive medical, dental, and vision insurance for you and your family 401(k) with generous employer match Company‑paid life insurance Flexible Spending Account Mental wellness benefits Flexible PTO A dynamic, innovative work culture focused on disruption Interested? If you're passionate about AI infrastructure and want to work with customers doing genuinely ground‑breaking work, I'd love to hear from you. Please get in touch to discuss this opportunity further. #J-18808-Ljbffr