Account Solution Architect

Join our team as an Account Solution Architect, where you'll work closely with an Account Executive and Account Manager to land new accounts and expand within them across Northern EMEA. You'll engage with AI labs, research institutions, and enterprises running demanding GPU workloads. This role requires technical expertise in ML training workflows, MLOps platforms, and GPU infrastructure, as well as strong communication skills and a collaborative mindset.

• Lead technical discovery with prospects and existing customers to understand model training requirements, MLOps workflows, and infrastructure constraints.
• Design and present solutions spanning ML infrastructure, GPU compute, storage, networking, Kubernetes-based orchestration, and MLOps tooling.
• Run proof-of-concept engagements and architecture reviews, and see them through to a decision.
• This is a role for someone who shows up with substance, who can hold a real conversation about model training and MLOps tooling in the morning and cluster networking in the afternoon, and who finds the space between infrastructure and application more interesting than either on its own
• Experience with AI frameworks such as PyTorch, TensorFlow, or JAX
• Strong communication skills - you can adjust your register from a research engineer to a VP of Infrastructure without losing credibility at either end
• Strong working knowledge of ML training and inference workflows - you understand how models are built, optimized, and deployed, not just where they run
• Hands-on experience with Kubernetes and containerized workloads at scale
• Fluency in English required; proficiency in Dutch, Swedish, Norwegian, Danish, or Finnish is a plus
• 3+ years in a pre-sales, solutions engineering, or solutions architecture role, ideally with a focus on AI/ML platforms, cloud infrastructure, or HPC
• Comfortable going below the SaaS layer when the conversation demands it - GPU topology, network fabric, and cluster architecture shouldn’t be unfamiliar territory
• Experience managing multiple concurrent opportunities across a geographic territory
• Hands-on familiarity with MLOps platforms and the tooling AI teams use day-to-day, including experiment tracking, pipeline orchestration, and model serving
• Familiarity with NVIDIA GPU architectures (H100, A100, H200) and the software stack around them: CUDA, NCCL, cuDNN
• Working knowledge of high-performance networking concepts: InfiniBand, RDMA, RoCE, TCP/IP
• Background working directly with AI labs, research institutions, or enterprise ML teams
• Exposure to physical AI use cases and the infrastructure requirements they bring
• Experience with Salesforce, Jira, Confluence, or similar tools
• Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams - even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk
• You’re a natural collaborator who makes the people around you - including your AE and AM - better at their jobs
• You’ve worked across more than one layer of the stack and find the intersections more interesting than any single discipline
• You thrive in a fast-moving environment and take ownership of your region like it’s your own business
• You’re comfortable going one layer deeper than the demo - whether that’s a networking topology, a training job that won’t converge, or a rack layout question from a facilities team