We are seeking a highly skilled Linux / HPC Infrastructure Engineer to support and scale enterprise AI/ML and LLM platforms across GPU clusters, Kubernetes environments, and data center infrastructure.

This role combines expertise in Linux systems, Kubernetes administration, HPC infrastructure, GPU-accelerated AI workloads, and enterprise platform operations.

You will be responsible for designing, deploying, optimizing, and maintaining highly available AI infrastructure environments supporting large-scale inference, distributed computing, and cloud-native AI platforms.

Key Responsibilities :

- Deploy, manage, and optimize AI/ML and LLM inference workloads across GPU clusters, HPC infrastructure, and cloud environments.

- Build and maintain scalable AI platform infrastructure using Kubernetes, containers, and enterprise orchestration platforms.

- Administer and optimize Linux servers including system configuration, patching, security hardening, performance tuning, and troubleshooting.

- Manage physical infrastructure including servers, storage, networking, and bare-metal environments within enterprise data centers.

- Implement and maintain CI/CD and automation workflows for platform and infrastructure deployments.

- Optimize infrastructure performance, GPU utilization, resource allocation, and distributed workloads to meet operational requirements.

- Benchmark and evaluate AI workloads for scalability, latency, throughput, and resource efficiency.

- Collaborate with infrastructure, SRE, and platform engineering teams to provision compute resources and maintain enterprise-scale AI environments.

- Implement monitoring, logging, observability, and alerting solutions for platform reliability and operational visibility.

- Apply security patches, upgrades, compliance controls, and operational best practices for Linux and Kubernetes environments.

- Troubleshoot issues across hardware, networking, operating systems, Kubernetes clusters, and AI/ML workloads.

- Support enterprise operations through efficient incident, change, and ticket management processes.

- Automate infrastructure operations using scripting and infrastructure automation tools.

Required Qualifications :

- 8+ years of experience in Linux systems administration, cloud-native infrastructure, HPC environments, or platform engineering.

- At least 4 years of experience supporting AI/ML workloads or large-scale distributed compute environments in production.

- Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring, and productivity enhancement.

- Strong hands-on expertise with Linux administration (RHEL, Ubuntu, or similar).

- Experience with Kubernetes administration, container orchestration, and cloud-native infrastructure platforms.

- Strong understanding of GPU infrastructure, distributed computing, and HPC systems.

- Hands-on experience with bare-metal infrastructure, servers, storage systems, and enterprise networking.

- Strong understanding of networking fundamentals including TCP/IP, DNS, load balancing, and firewalls.

- Experience with scripting and infrastructure automation with Bash, Python etc.

- Experience with CI/CD, DevOps, or infrastructure deployment workflows.

- Experience with monitoring, observability, and logging platforms.

- Strong troubleshooting and performance optimization skills across Linux, Kubernetes, networking, and infrastructure stacks.

- Excellent problem-solving, communication, and collaboration skills.

- Ability to work effectively in fast-paced, mission-critical production environments.

Ways to Stand Out from the Crowd :

- Experience with OpenShift or large-scale Kubernetes platform operations.

- Hands-on experience supporting AI/ML and LLM inference platforms at scale, including working with vLLM for high-performance LLM serving, optimization, and large-scale inference.

- Experience with infrastructure-as-code, GitOps, or advanced automation frameworks.

- Proficiency with Python, Bash, Ansible, Jenkins, Git, ArgoCD, or similar DevOps tooling.

- Experience with GPU performance tuning, distributed AI workloads, or HPC optimization.

- Familiarity with observability platforms such as Prometheus, Grafana, ELK/Elasticsearch, or distributed tracing systems.

- Experience supporting enterprise AI infrastructure in hybrid cloud or large-scale data center environments.

- Familiarity with Juniper networking environments or enterprise network operations.

- RHCSA/RHCE, CKA/CKS, or similar infrastructure/platform certifications.