What are the responsibilities and job description for the Contract W2: HPC AI/ML Platform Engineer- Remote position at KPG99 INC?
HPC AI/ML Platform Engineer
12 month contract
100% remote
USC/GC
Top Skills' Details
Client is seeking experienced HPC AI/ML Platform Engineers to support one of the world's largest AI compute environments. This team operates and scales multi-cloud infrastructure powering GPU-intensive model training, deep learning research, and production AI workloads across AWS, CoreWeave, GCP, OCI, and other emerging compute providers.
This is not a traditional cloud infrastructure role. The ideal candidate has hands-on experience supporting High-Performance Computing (HPC), deep learning platforms, large-scale GPU environments, and AI/ML training infrastructure where performance, reliability, and efficient resource utilization are mission critical.
Engineers on this team will help provision and operate large-scale compute environments, manage Kubernetes-based infrastructure, optimize GPU resource allocation, support Slurm scheduling systems, and build automation that enables rapid AI platform growth.
Success in this role requires experience operating production environments that support AI model development, training, and inference workloads at significant scale.
Must Have Requirements
Proven experience supporting High Performance Computing (HPC) environments at enterprise scale
Experience supporting AI/ML or Deep Learning infrastructure, including model training workloads
Provision HPC infrastructure through CI/CD systems across AWS, CoreWeave, GCP, OCI, and additional cloud providers
Experience managing or supporting Slurm-based job scheduling environments
Operate and troubleshoot large-scale Kubernetes platforms supporting GPU-intensive workloads
Define and maintain SLIs/SLOs, monitoring, alerting, incident response, and post-incident reviews
Partner closely with Networking, Storage, Security, and AI/ML Platform teams
Experience operating environments where performance, cluster stability, and GPU utilization directly impact business outcomes
Requirements
4 years supporting HPC, AI/ML Infrastructure, Machine Learning Platforms, or Deep Learning environments
Strong experience with distributed compute, GPU infrastructure, or large-scale training environments
Hands-on Kubernetes Experience At Scale Including
Cluster lifecycle management
Scheduler troubleshooting
Node pool management
Networking and CNI troubleshooting
Production upgrades across large fleets
Terraform experience building and managing infrastructure through Infrastructure as Code
Python experience for platform automation and operational tooling
Working knowledge of AWS services including EC2, S3, EFS, and FSx for Lustre
Experience With Slurm Job Scheduling Strongly Preferred
Experience supporting ML training, inference, deep learning, large language model (LLM), or GPU-intensive workloads preferred
Business Drivers/Customer Impact
The client's AI infrastructure footprint is growing faster than operational capacity. As GPU clusters continue to scale, the organization requires engineers with deep HPC and AI infrastructure expertise to ensure efficient resource utilization, platform reliability, and uninterrupted model training operations. Failures within these environments can result in significant GPU underutilization, degraded research velocity, and increased operational risk.
12 month contract
100% remote
USC/GC
Top Skills' Details
- HPC / AI Infrastructure Experience (Must Have): Hands-on experience supporting High Performance Computing (HPC), AI/ML, Deep Learning, or GPU-intensive production environments at enterprise scale. Candidates should demonstrate experience supporting model training workloads, distributed compute platforms, or large-scale AI infrastructure.
- Kubernetes at Scale: Strong experience operating production Kubernetes platforms supporting high-performance workloads, including cluster lifecycle management, scheduling, networking, upgrades, troubleshooting, and platform reliability.
- Slurm AI Compute Operations: Experience supporting Slurm or similar workload schedulers used to allocate compute resources across AI model training and inference workloads. Understanding of GPU utilization, workload orchestration, and distributed compute environments is highly desired.
- Infrastructure as Code & Automation: Strong Terraform and Python experience for provisioning, managing, automating, and scaling infrastructure environments.
Client is seeking experienced HPC AI/ML Platform Engineers to support one of the world's largest AI compute environments. This team operates and scales multi-cloud infrastructure powering GPU-intensive model training, deep learning research, and production AI workloads across AWS, CoreWeave, GCP, OCI, and other emerging compute providers.
This is not a traditional cloud infrastructure role. The ideal candidate has hands-on experience supporting High-Performance Computing (HPC), deep learning platforms, large-scale GPU environments, and AI/ML training infrastructure where performance, reliability, and efficient resource utilization are mission critical.
Engineers on this team will help provision and operate large-scale compute environments, manage Kubernetes-based infrastructure, optimize GPU resource allocation, support Slurm scheduling systems, and build automation that enables rapid AI platform growth.
Success in this role requires experience operating production environments that support AI model development, training, and inference workloads at significant scale.
Must Have Requirements
Proven experience supporting High Performance Computing (HPC) environments at enterprise scale
Experience supporting AI/ML or Deep Learning infrastructure, including model training workloads
Provision HPC infrastructure through CI/CD systems across AWS, CoreWeave, GCP, OCI, and additional cloud providers
Experience managing or supporting Slurm-based job scheduling environments
Operate and troubleshoot large-scale Kubernetes platforms supporting GPU-intensive workloads
Define and maintain SLIs/SLOs, monitoring, alerting, incident response, and post-incident reviews
Partner closely with Networking, Storage, Security, and AI/ML Platform teams
Experience operating environments where performance, cluster stability, and GPU utilization directly impact business outcomes
Requirements
4 years supporting HPC, AI/ML Infrastructure, Machine Learning Platforms, or Deep Learning environments
Strong experience with distributed compute, GPU infrastructure, or large-scale training environments
Hands-on Kubernetes Experience At Scale Including
Cluster lifecycle management
Scheduler troubleshooting
Node pool management
Networking and CNI troubleshooting
Production upgrades across large fleets
Terraform experience building and managing infrastructure through Infrastructure as Code
Python experience for platform automation and operational tooling
Working knowledge of AWS services including EC2, S3, EFS, and FSx for Lustre
Experience With Slurm Job Scheduling Strongly Preferred
Experience supporting ML training, inference, deep learning, large language model (LLM), or GPU-intensive workloads preferred
Business Drivers/Customer Impact
The client's AI infrastructure footprint is growing faster than operational capacity. As GPU clusters continue to scale, the organization requires engineers with deep HPC and AI infrastructure expertise to ensure efficient resource utilization, platform reliability, and uninterrupted model training operations. Failures within these environments can result in significant GPU underutilization, degraded research velocity, and increased operational risk.