Lead Cloud Platform Engineer

Description

Role: HPC (High-Performance Computing) Consultant (5+ Openings)

Location: Remote

Duration: Fulltime

Work Type: Remote-first, with occasional on-site visits to customer offices.

We are looking for individual contributor with expertise across Kubernetes, cloud infrastructure, HPC platforms, GPU computing, Terraform, and Python Coding. Depending on experience, candidates may be considered for Platform Engineer, Kubernetes Engineer, HPC Engineer, Cloud Infrastructure Engineer, DevOps Engineer, AI Infrastructure Engineer, or Site Reliability Engineer (SRE) roles.

Key Responsibilities

• Design, deploy, operate, and support large-scale Kubernetes platforms across AWS, GCP, CoreWeave, OCI, and other cloud environments.
• Manage Kubernetes cluster lifecycle activities including provisioning, scaling, node pool management, upgrades, troubleshooting, and performance optimization.
• Support AI/ML and HPC workloads, including GPU-enabled compute infrastructure for training and inference environments.
• Provision and automate cloud and infrastructure resources using Terraform and CI/CD pipelines.
• Troubleshoot Kubernetes scheduling, networking, storage, and platform reliability issues.
• Implement monitoring, observability, alerting, SLIs/SLOs, and incident response processes.
• Collaborate with Networking, Security, Storage, AI/ML, Data Engineering, and Application teams.
• Develop automation and operational tooling using Python and cloud-native technologies.
• Participate in production support, root cause analysis, capacity planning, and platform optimization initiatives.

Required Skills

Kubernetes & Container Platforms

• Kubernetes (EKS, GKE, AKS, OpenShift, CoreWeave CKS)
• Cluster Lifecycle Management
• Node Pool Management
• Scheduler Troubleshooting
• CNI Troubleshooting
• Networking Policies
• RBAC
• Helm
• Autoscaling
• Rolling Upgrades

Cloud Infrastructure

• AWS (EC2, S3, IAM, VPC, EKS, EFS, FSx for Lustre)
• Google Cloud Platform (GCP)
• OCI (Oracle Cloud Infrastructure)
• Azure (Preferred)
• Multi-Cloud Infrastructure

Infrastructure as Code & Automation

• Terraform
• Infrastructure as Code (IaC)
• CI/CD Pipelines
• GitHub Actions / Jenkins / GitLab CI
• Ansible (Preferred)

Programming & Scripting

• Python
• Bash/Shell Scripting
• Automation Development
• REST API Integration

HPC & GPU Infrastructure (Preferred)

• High-Performance Computing (HPC)
• Slurm
• NVIDIA GPU Platforms
• GPU Scheduling
• CUDA
• Distributed Computing
• AI/ML Infrastructure

Monitoring & Reliability

• Prometheus
• Grafana
• Datadog
• Splunk
• Cloud Monitoring
• SLI/SLO Management
• Incident Response
• Root Cause Analysis (RCA)

Preferred Experience

Experience with any of the following is highly desirable:

• AI/ML Infrastructure Platforms
• Kubeflow
• KServe
• Ray
• MLflow
• vLLM
• Vector Databases
• Distributed Training Platforms
• CoreWeave
• AWS ParallelCluster
• FSx for Lustre
• Lustre
• WekaFS
• InfiniBand
• RDMA
• AI Training & Inference Workloads