Remote Principal AI/ML Platform Engineer

Optum; Eden Prairie (MN); Remote; USD 165,000 - 282,000; Full time

Benefits offered by this job

Equity stock purchase

401k contribution

Comprehensive benefits package

Remote work option

Job summary

Optum is seeking a Principal AI/ML Platform Engineer to lead the end-to-end reference architecture for multi-tenant AI compute platforms across hybrid OpenShift clusters and public-cloud AI services. You will drive network standards, governance, and automated lifecycle management to ensure scalable, secure AI workloads.

Responsibilities include cost modeling, model-serving standardization, workload placement decisions, and robust IAM controls.

Qualifications

• Bachelor’s degree or 4+ years of equivalent software/platform engineering experience in lieu of a degree.
• 10+ years of experience in infrastructure, DevOps, SRE, or ML platform engineering.
• 5+ years of experience operating Kubernetes or OpenShift at scale in production bare-metal or enterprise cloud environments.
• 3+ years of experience designing and managing accelerated-compute (AI/GPU) infrastructure utilizing NVIDIA GPU Operator, NFD, MIG/time-slicing, and DCGM.
• 3+ years of experience architecting hybrid AI platforms spanning self-hosted IaaS and public-cloud PaaS managed AI services (e.g., Azure AI Foundry, AWS Bedrock, or GCP Vertex AI).
• 3+ years of experience with HPC/AI networking technologies, including InfiniBand or RoCEv2, GPUDirect RDMA, and NCCL collective communication limits.
• 3+ years of experience managing multi-cluster fleets using RHACM (or equivalent) and GitOps tooling (Argo CD or Flux).
• 3+ years of experience implementing enterprise security and IAM controls for software workloads (RBAC, OIDC/OAuth, Vault secrets management, mTLS).

Responsibilities

• Own the end-to-end reference architecture for multi-tenant AI compute platforms across hybrid on-premises bare-metal OpenShift AI clusters and public-cloud managed AI platforms (Azure AI Foundry, AWS Bedrock, GCP Vertex AI).
• Set network, latency, and topology standards for distributed training (NVLink, InfiniBand, RoCEv2, GPUDirect RDMA, NCCL), ensuring interconnect boundaries are strictly maintained.
• Establish cluster governance, GitOps workflows (Argo CD), RHACM policies, and automated lifecycle management for bare-metal accelerated compute nodes.
• Design cost and utilization models including capex amortization, accelerator-sharing strategies (MIG/time-slicing), and cost-per-token/training-run math to inform accelerator procurement roadmaps.
• Standardize model-serving platforms (vLLM, KServe) and inference gateways, setting quantization policies, provenance review gates, and intelligent model routing rules.
• Define workload placement frameworks to determine self-hosted versus managed cloud deployment based on data residency, latency, cost, and compliance requirements.
• Design and enforce identity, access, and security controls for AI workloads and autonomous agents, including least-privilege RBAC, Vault secret management, short-lived credentials, and mTLS.
• Establish enterprise Service Level Objectives (SLOs), disaster recovery plans, and upgrade strategies for OpenShift, OpenShift AI, GPU operators, drivers, and firmware.
• Partner with cross-functional AI teams, LLM gateway engineers, privacy, and security stakeholders to ensure seamless integration and HIPAA compliance.

Skills

Kubernetes

OpenShift

RHACM

GitOps

Security controls

Education

Bachelor’s degree or equivalent experience

Tools

Kubernetes

OpenShift

ARgo CD

Job description

Optum is seeking a Principal AI/ML Platform Engineer to lead the end-to-end reference architecture for multi-tenant AI compute platforms across hybrid OpenShift clusters and public-cloud AI services. You will drive network standards, governance, and automated lifecycle management to ensure scalable, secure AI workloads.

Responsibilities include cost modeling, model-serving standardization, workload placement decisions, and robust IAM controls.