AI Infrastructure Consulting Engineer – Orchestration & Platform

The AI Infrastructure Consulting Engineer – Orchestration & Platform Engineering is responsible for implementing, integrating, and supporting platforms that schedule, govern, and operate enterprise AI workloads. This client-facing, billable role delivers high-quality technical outcomes and ensures solutions are implemented as designed to the client’s satisfaction and approval. The engineer works across Kubernetes-based and workload-scheduling environments to enable reliable, secure, multi-user access to accelerated computing resources.

• Manage time and expense to meet or exceed expectations defined in the Statement of Work, proactively escalating potential scope, schedule, or budget issues as soon as they are known.
• Implement and configure AI workload orchestration, scheduling, resource governance, and platform services across customer environments.
• Deploy and integrate Kubernetes, Run:ai, ClearML, Slurm, NVIDIA software components, container registries, observability, and supporting platform services as applicable to the engagement.
• Perform platform discovery, installation, configuration, validation, upgrades, troubleshooting, and client knowledge transfer.
• Collaborate with security, networking, compute, storage, application, and data teams to resolve cross-platform dependencies.
• Provide technical input for SOWs, ROMs, LOEs, implementation plans, and solution validation when requested.
• Create and maintain accurate project documentation, as-built configurations, test plans, work logs, and client handoff materials.

• Bachelor’s degree and 3 years’ experience delivering complex platform, infrastructure, or cloud-native solutions, including planning, implementation, integration, and support OR 7 years’ experience delivering complex platform, infrastructure, or cloud-native solutions, including planning, implementation, integration, and support.
• Hands‑on experience administering Kubernetes or enterprise Kubernetes distributions in production or customer environments.
• Experience with containers, registries, Helm, operators, storage classes, ingress, identity integration, secrets, and role‑based access control.
• Working knowledge of GPU scheduling, quotas, tenancy, resource allocation, job lifecycle, and utilization concepts in AI environments.
• Experience with one or more workload platforms such as Run:ai, ClearML, Slurm, NVIDIA Base Command, or comparable orchestration and scheduling technologies.
• Working knowledge of Linux, scripting, APIs, Git‑based workflows, and infrastructure automation.
• Ability to build and execute implementation and test plans aligned to assigned project deliverables and the Statement of Work.
• Strong written and verbal communication skills with the ability to effectively interact with leadership stakeholders and clients.
• Proven ability to create technical documentation and transfer knowledge to administrators and platform users.
• Demonstrated attention to detail, analytical problem solving, and the ability to balance competing priorities while meeting deadlines.
• Ability and willingness to work beyond regularly scheduled shifts, select weekends, or overtime as necessary to meet client needs.
• Ability and willingness to travel as needed to CDW locations or client sites.
• Valid U.S. driver’s license.
• Experience integrating Kubernetes with NVIDIA GPU Operator, Network Operator, device plugins, or NVIDIA AI Enterprise components is a plus.
• Experience with platform observability, policy, security, identity, MLOps, CI/CD, and infrastructure‑as‑code tooling is a plus.
• Familiarity with Kubeflow, MLflow, Argo, Git Ops, Prometheus, Grafana, or comparable ecosystem tools is a plus.
• Relevant Kubernetes, NVIDIA, Linux, or automation certifications are a…