Kubernetes Architect (AI / GPU Platforms)

Kubernetes Architect (AI / GPU Platforms)

Location: Dallas, TX (Hybrid – 3/2) | Relocation available

Type: Direct Hire

• $175K–$250K base + performance bonus

• 100% company-paid benefits

Overview

We are seeking a Kubernetes Architect to lead the design and delivery of GPU-accelerated container platforms supporting next-generation AI, machine learning, and high-performance computing workloads.

This organization operates at the forefront of large-scale compute infrastructure, building platforms that power scientific research, advanced simulation, and data-intensive innovation. This role sits at the intersection of Kubernetes, HPC, and GPU infrastructure, driving architecture decisions that directly impact performance, scalability, and multi-tenant platform efficiency.

This is a customer-facing architecture role with ownership across the full solution lifecycle, from early discovery and requirements definition through design, proof-of-concept, deployment, and long-term optimization. You will serve as a trusted advisor to both internal stakeholders and external users, shaping how GPU-based Kubernetes platforms are built and scaled across complex environments.

Key Responsibilities

Architecture & Customer Engagement

• Serve as the primary architectural lead for GPU-accelerated Kubernetes platforms supporting HPC and AI/ML workloads

• Translate complex workload requirements into scalable, production-ready reference architectures

• Lead discovery sessions, technical design workshops, and performance benchmarking engagements

• Guide customers through platform adoption, integration, and long-term optimization strategies

• Present architectural solutions and act as a subject matter expert in Kubernetes-based HPC environments

Kubernetes & GPU Platform Engineering

• Design and optimize Kubernetes clusters for GPU-intensive workloads in on-prem and hybrid environments

• Implement and tune NVIDIA ecosystem components, including GPU Operator, DCGM, MIG, and device plugins

• Optimize GPU scheduling and utilization through Kubernetes extensions (Volcano, Slurm integration, scheduler plugins)

• Develop and extend Kubernetes operators and controllers (Go/Python) to automate infrastructure services

Infrastructure Integration (Compute, Storage, Network)

• Architect end-to-end platform integration across compute, storage, and networking layers

• Integrate high-performance storage solutions (Lustre, GPFS, Ceph, VAST) into Kubernetes environments

• Design and support high-performance networking (InfiniBand, RDMA, RoCE, NVLink) for distributed workloads

• Define multi-tenant architectures with strong isolation, security, and resource governance (RBAC, OPA/Gatekeeper)

Observability, Automation & Performance

• Implement observability frameworks using Prometheus, Grafana, DCGM Exporter, and OpenTelemetry

• Drive workload profiling, benchmarking, and performance tuning across distributed compute environments

• Support GitOps-based deployment models using ArgoCD, FluxCD, Helm, and Kustomize

• Partner with HPC and ML teams to validate performance and scalability at production scale

Ecosystem & Product Collaboration

• Collaborate with internal product, engineering, and operations teams to influence platform roadmap

• Engage with key ecosystem partners (NVIDIA, networking and storage vendors) to integrate emerging technologies

• Provide forward-looking guidance on GPU architectures, interconnect evolution, and orchestration trends

Required Experience

• Extensive experience designing and operating Kubernetes platforms in HPC or GPU-intensive environments

• Deep expertise with the NVIDIA GPU ecosystem (GPU Operator, MIG, DCGM, device plugins)

• Strong understanding of Kubernetes internals, including CRDs, RBAC, scheduling, and custom controllers

• Experience integrating distributed storage systems for high-performance workloads

• Strong knowledge of high-performance networking (InfiniBand, RDMA, RoCE) in containerized environments

• Proven ability to design scalable, secure, and highly available distributed compute platforms

• Proficiency in Go or Python for infrastructure automation or operator development

• Experience with workload benchmarking, profiling, and performance optimization

• Strong communication skills with the ability to translate complex technical concepts into actionable solutions

Preferred Experience

• Experience delivering end-to-end customer solutions from design through deployment and adoption

• Familiarity with HPC workload orchestration tools (Slurm, Kubernetes schedulers, Apptainer/Singularity)

• Exposure to GitOps and infrastructure-as-code practices in Kubernetes environments

• Contributions to open-source Kubernetes or GPU ecosystem projects

• Experience advising on long-term platform strategy and emerging technology adoption

• Relevant certifications such as CKA, CKAD, CKS, or cloud architecture certifications (AWS, Azure)

Why This Role

• High-impact role shaping next-generation AI and HPC infrastructure

• Direct influence on platform architecture, performance, and scalability at scale

• Strong visibility across engineering, product, and customer environments

• Backed by significant investment and long-term growth in AI compute platforms