Principal AI/ML Platform Engineer - Remote

UnitedHealth Group•Eden Prairie, MN

19h•$164,600 - $282,200•Remote

About The Position

As a Principal AI/ML Platform Engineer on the UnitedHealth Group (UHG) enterprise team, you will serve as the AI Architect across IaaS and PaaS environments, owning the technical direction, reference architecture, and long-term evolution of our multi-tenant AI compute platform end to end. Our team builds and maintains an advanced compute estate spanning on-premises bare-metal Red Hat OpenShift AI clusters equipped with high-performance NVIDIA GPUs and InfiniBand/RoCE training fabrics, alongside public-cloud managed AI services including Azure AI Foundry, AWS Bedrock, and GCP Vertex AI. In this role, you will define architecture standards, optimize high-throughput model training and inference pipelines, establish cost and utilization economics, and enforce strict HIPAA, security, and data-governance standards for regulated healthcare workloads. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Requirements

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)

Nice To Haves

Experience with distributed training frameworks (PyTorch DDP/FSDP, DeepSpeed, Ray, JAX) and batch scheduling systems (Kueue, Volcano)
Hands-on experience with LLM inference serving technologies (vLLM, TensorRT-LLM, KServe) and platform tooling such as OpenShift AI (RHOAI) or Kubeflow pipelines
Experience with high-performance parallel storage systems (Ceph/ODF, Lustre, IBM Storage Scale, VAST, WEKA)
Active Red Hat certifications (e.g., Red Hat Certified Architect / RHCA) or open-source contributions to CNCF, OpenShift, or AI infrastructure projects
Experience operating AI/ML platforms within regulated healthcare environments under HIPAA and UHG data privacy controls

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