Accelerated Computing Engineer

Roles & Responsibilities:

1. Cluster and GPU Management :

• •Launch, validate, and maintain GPU-based AI/ML training clusters (8xH100, 8xH200, 32xH200, 64xH200 upto 1024 H200s).

• •Verify all cluster nodes have InfiniBand enabled and GPUs correctly assigned (no Ethernet fallback).

• •Ensure Slurm deployments are up within a minute and all workers are ready (sinfo shows all active).

• •Validate DGCX Workbench runs successfully for:
• •Llama3-8B on 8xH100 / 8xH200 cluster
• •Llama3-70B on 32xH200 / 64xH200 clusters

Monitor GPU health using tools and validate performance benchmarks.

Maintain cluster reliability — all training and inference nodes should remain up and restartable

without failure.

2. Inference and Endpoint Operations :

• •Launch and monitor vLLM inference endpoints (e.g., Llama 70B) ensuring:
• •First startup within 10 minutes
• • Restart within 1 minute
• •Autoscale brings new workers up within 3 minutes
• •Inference endpoints remain continuously reachable and 100% ready
• • Troubleshoot and stabilize stateful workloads, notebooks, and AI services.

3. Customer-Facing Technical Support :

• •Engage directly with customers via calls, video meetings (Google Meet/Hangout), and screen-sharing sessions.
• •Understand the customer’s problem in real time and guide them through the solution.
• • Diagnose complex GPU, Slurm, or inference issues and resolve them collaboratively on the call.
• •Provide clear updates and ensure timely resolution of support tickets.
• • Document RCA and contribute to permanent fixes or product improvements.
• • Communicate professionally and technically with data scientists, developers, and enterprise users.Automation and Reliability
• • Automate cluster provisioning and monitoring using Terraform, Ansible, and Python.
• •Create scripts for routine cluster health checks, GPU utilization, and job queue validation.
• •Collaborate with the platform and DevOps teams to implement improvements for speed and reliability.

Key Skills & Qualifications:

• •2–4 years of experience in GPU-based cloud operations, MLOps, or infrastructure engineering.

• •Prior exposure to customer-facing roles or live technical troubleshooting calls.

• •Experience working with AI model training pipelines, inference endpoints, or Slurm-managed clusters.

• •Familiarity with LLM workloads such as Llama, Mistral, or Falcon models.

• •Linux (Ubuntu/CentOS), system performance tuning

• •Networking: InfiniBand, VLAN, VPN, ALB, DNS, NAT

• •Containers & orchestration: Docker, Kubernetes, Helm

• •GPU operations: CUDA, GPU drivers, nvidia-smi, MIG configuration

• •Distributed training: Slurm, DDP (Distributed Data Parallel) concepts

• •AI Inference: vLLM, TensorRT, ONNX Runtime, Hugging Face models

• •Infrastructure as Code: Terraform, Ansible

• •Tools: ssh, curl, tcpdump, Prometheus, Grafana, ELK