Infrastructure Engineer

HCLTech; California (MO); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401(k) Retirement Plan

Paid Time Off and Holidays

Job summary

HCLTech is seeking an experienced AI Infrastructure Engineer (L3) to design, deploy, and optimize large-scale GPU-based AI infrastructure. The role covers GPU clusters, CUDA/TensorRT stacks, storage, and high-speed networking across Linux environments in cloud-native contexts.

Ideal candidates will have extensive Kubernetes experience, proficiency with Terraform/Helm/ArgoCD, and a track record of production-grade AI platforms.

Qualifications

• Bachelor's Degree in Computer Science, Engineering, or a related field.
• 8-12 years of Infrastructure or Platform Engineering experience.
• 4-6 years supporting AI/ML environments and GPU-based platforms.
• Experience operating production-scale AI infrastructure.
• Strong Linux administration and performance tuning skills.
• Experience operating Kubernetes and cloud-native technologies.
• Hands-on experience with Terraform, Helm, ArgoCD, and automation frameworks.

Responsibilities

• Deploy and manage NVIDIA GPU infrastructure and AI accelerator platforms.
• Administer Kubernetes GPU clusters using NVIDIA GPU Operator.
• Install and maintain CUDA, cuDNN, TensorRT, firmware, and driver stacks.
• Manage high-performance storage such as Ceph, Lustre, BeeGFS, and NFS.
• Support InfiniBand, RDMA, RoCE, NVLink, and other high-speed networking.
• Optimize Linux environments for AI and HPC workloads.
• Support AI orchestration platforms like Kubeflow, MLflow, Ray, and Slurm.
• Implement Infrastructure as Code using Terraform, Helm, and GitOps.
• Monitor platform performance with Prometheus, Grafana, NVIDIA DCGM, and OpenTelemetry.
• Lead root cause analysis and resolve GPU, networking, storage, and platform issues.
• Collaborate with cloud, data science, MLOps, SRE, and engineering teams to deliver scalable AI platforms.

Skills

NVIDIA GPU platforms

Kubernetes

CUDA

TensorRT

HPC environments

Linux performance tuning

Terraform

Helm

ArgoCD

OpenTelemetry

Prometheus

NVIDIA DCGM

Education

Bachelor's Degree in Computer Science or Engineering

Tools

NVIDIA GPU Operator

Kubernetes

Terraform

Helm

ArgoCD

GitOps

Prometheus

Grafana

NVIDIA DCGM

Job description

HCLTech is a global technology company with over 220,000 professionals across 60 countries, delivering industry-leading capabilities in Digital, Engineering, Cloud, and AI. We help enterprises accelerate innovation through cutting-edge technologies and world-class talent.

Job Summary

We are seeking an experienced AI Infrastructure Engineer (L3) to design, deploy, optimize, and support high-performance AI and Machine Learning infrastructure. The ideal candidate will have deep expertise in GPU platforms, Kubernetes, HPC environments, distributed systems, and cloud-native AI technologies. This role involves managing large-scale GPU clusters, supporting AI training and inference workloads, troubleshooting complex infrastructure issues, and driving platform reliability.

Key Responsibilities

• Deploy and manage NVIDIA GPU infrastructure (A100, H100, L40) and AI accelerator platforms.
• Administer Kubernetes GPU clusters using NVIDIA GPU Operator and related technologies.
• Install and maintain CUDA, cuDNN, TensorRT, firmware, and driver stacks.
• Manage high-performance storage solutions such as Ceph, Lustre, BeeGFS, and NFS.
• Support InfiniBand, RDMA, RoCE, NVLink, and other high-speed networking technologies.
• Optimize Linux environments (RHEL, Ubuntu, Rocky Linux) for AI and HPC workloads.
• Support AI orchestration platforms including Kubeflow, MLflow, Ray, and Slurm.
• Implement Infrastructure as Code using Terraform, Helm, and GitOps tools.
• Monitor platform performance with Prometheus, Grafana, NVIDIA DCGM, and OpenTelemetry.
• Lead root cause analysis (RCA) and resolve critical GPU, networking, storage, and platform issues.
• Collaborate with cloud, data science, MLOps, SRE, and engineering teams to deliver scalable AI platforms.

Required Skills

• Strong experience with NVIDIA GPU platforms and GPU cluster administration.
• Expertise in Kubernetes, containerization, and cloud-native technologies.
• Hands-on experience with CUDA, TensorRT, NCCL, DeepSpeed, Horovod, and distributed training.
• Strong Linux administration and performance tuning skills.
• Experience with Terraform, Helm, ArgoCD, and automation frameworks.
• Knowledge of AI infrastructure, MLOps, and large-scale distributed systems.
• Excellent troubleshooting, debugging, and production support experience.

Preferred Certifications

• NVIDIA Certified Associate – AI Infrastructure
• NVIDIA Base Command Manager Certification
• AWS Solutions Architect Associate

Qualifications

• Bachelor's Degree in Computer Science, Engineering, or a related field.
• 8-12 years of Infrastructure or Platform Engineering experience.
• 4-6 years supporting AI/ML environments and GPU-based platforms.
• Experience operating production-scale AI infrastructure.

Disclaimer

HCL is an equal opportunity employer, committed to providing equal employment opportunities to all applicants and employees regardless of race, religion, sex, color, age, national origin, pregnancy, sexual orientation, physical disability or genetic information, military or veteran status, or any other protected classification, in accordance with federal, state, and/or local law. Should any applicant have concerns about discrimination in the hiring process, they should provide a detailed report of those concerns to [e-mail] for investigation.

Compensation and Benefits

A candidate’s pay within the range will depend on their work location, skills, experience, education, and other factors permitted by law. This role may also be eligible for performance-based bonuses subject to company policies. In addition, this role is eligible for the following benefits subject to company policies: medical, dental, vision, pharmacy, life, accidental death & dismemberment, and disability insurance; employee assistance program; 401(k) retirement plan; 10 days of paid time off per year (some positions are eligible for need-based leave with no designated number of leave days per year); and 10 paid holidays per year.