Senior Solutions Architect - Post-sales

EMEA

About the Role

We are seeking a Senior Solutions Architect to help customers successfully deploy, operate, and scale AI/ML workloads on our GPU Platform-as-a-Service (PaaS) offering. In this customer-facing role, you will work closely with platform engineering, MLOps, data science, and infrastructure teams to design and implement production-ready AI infrastructure solutions built on Kubernetes and GPU-accelerated environments.

As a Senior Solutions Architect, you will serve as a trusted advisor to enterprise customers, lead complex deployments, drive architecture best practices, and help customers maximize the value of their AI infrastructure investments. You will help customers onboard to the platform, optimize workload performance, automate infrastructure, and ensure reliable operations while serving as a strategic technical partner throughout the customer lifecycle.

Responsibilities

• Design end-to-end AI/ML platform architectures spanning inference, training, and data pipelines
• Develop reference architectures for GPU cluster deployment, LLM serving, and multi-tenant ML infrastructure
• Evaluate and recommend inference serving frameworks (vLLM, TGI, Triton, NIM)
• Advise on GPU fabric topology — NVLink, InfiniBand, RoCEv2 — for distributed training
• Design observability strategies across DCGM, OTel, eBPF, and GPU metrics pipelines
• Translate complex infrastructure requirements into actionable platform designs
• Deliver technical presentations, workshops, and proof-of-concept engagements
• Act as a trusted advisor on AI infrastructure strategy, cost, and scaling
• Partner with customer platform, MLOps, data science, and executive stakeholders to understand AI/ML workload requirements and translate them into scalable platform architectures.
• Architect networking, identity management, observability, and security integrations with enterprise systems.
• Monitor and troubleshoot production environments, including GPU utilization, workload performance, cluster health, and cost efficiency.
• Lead root cause analysis and remediation efforts for complex customer issues.
• Serve as the primary technical advisor and escalation point for assigned customers.
• Provide technical leadership during customer engagements and drive adoption of platform best practices.
• Document reference architectures, implementation guides, and best practices.
• Provide feedback to Product and Engineering teams to improve platform capabilities and influence product roadmap decisions.
• Collaborate with internal teams to ensure successful customer adoption, expansion, and long-term success.
• Mentor junior team members and contribute to the growth of the Solutions Architecture organization.

Required Qualifications

• 8+ years in infrastructure, platform, or solutions engineering roles
• 3+ years focused on AI/ML infrastructure or MLOps
• Deep Kubernetes expertise — cluster lifecycle, workloads, operators, RBAC
• Hands-on experience with NVIDIA GPU infrastructure (H100/H200/B200 preferred)
• Proficiency with distributed training concepts — NCCL, tensor parallelism, pipeline parallelism
• Experience with LLM inference serving and optimization (vLLM, NIM, TGI)
• Familiarity with GPU Operator, MIG, SR-IOV, and network fabrics (IB/RoCEv2)
• Strong scripting and automation skills (Python, Bash, Go preferred)
• Demonstrated ability to communicate complex technical concepts to diverse audiences
• Experience with at least one programming language such as Python or Go.
• Experience with AWS, Azure, or GCP, including networking, IAM, and managed Kubernetes services.
• Familiarity with monitoring and observability technologies including Prometheus, Grafana, OpenTelemetry, or similar.
• Strong understanding of AI/ML infrastructure concepts including GPU-based workloads, model serving, training pipelines, and resource optimization.
• Proven ability to troubleshoot and resolve complex infrastructure and platform issues.
• Excellent communication, presentation, and customer-facing skills.
• Experience leading technical discussions with both engineering teams and executive stakeholders.

Preferred Qualifications

• Experience supporting enterprise customers in cloud-native environments.
• Familiarity with AI/ML frameworks such as PyTorch and TensorFlow.
• Experience with Run:AI, Slurm,
• Experience with GPU scheduling, autoscaling, and workload optimization.
• Understanding of multi-tenant Kubernetes environments and platform operations.
• Experience working with MLOps or AI infrastructure platforms.
• Experience developing reference architectures and leading technical workshops.
• Relevant certifications such as CKA, CKAD, AWS Solutions Architect, Azure Solutions Architect, or GCP Professional Cloud Architect.
• Understanding of multi-tenant GPU isolation (SR-IOV VFs, DPU offload)

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