What are the responsibilities and job description for the Senior Engineer AI Workload & Infrastructure Validation position at VST Consulting, Inc?
Role Name :Senior Engineer AI Workload & Infrastructure Validation
Location: Plano, TX (Hybrid - 2 Days Onsite / 3 Days Remote)
Employment Type: Full-Time
Compensation: Open / Best in the Industry
Interview Mode: Virtual
Practice: AI Infrastructure / GPU-as-a-Service (GPUaaS)
Employment Type: Full-Time
Compensation: Open / Best in the Industry
Interview Mode: Virtual
Practice: AI Infrastructure / GPU-as-a-Service (GPUaaS)
Job Summary
We are seeking a Senior Engineer to drive AI workload optimization, distributed training enablement, infrastructure validation, benchmarking, and performance analysis for enterprise GPU deployments. This role will help define customer validation standards and benchmark methodologies for AI platforms.
Key Responsibilities
- Enable and optimize distributed AI training using PyTorch DDP, FSDP, DeepSpeed, and NCCL.
- Deploy and optimize inference platforms using Triton, NVIDIA NIM, vLLM, and TensorRT-LLM.
- Build benchmark and validation frameworks for GPU infrastructure.
- Perform GPU, storage, networking, and cluster performance validation.
- Produce customer-facing performance reports and acceptance documentation.
- Design proof-of-concept workloads and benchmark environments.
- Support customer AI initiatives including RAG, Fine-Tuning, Agentic AI, and Vector Database integrations.
- Establish best practices for infrastructure validation and performance engineering.
Required Qualifications
- 5 years of experience in ML Engineering, MLOps, AI Infrastructure, or Performance Engineering.
- Hands-on experience with distributed training across multi-node GPU clusters.
- Experience deploying production LLM inference platforms.
- Strong understanding of benchmarking and performance analysis.
- Strong Python development experience.
- Experience with Kubernetes-native AI workflows.
Required Skills
PyTorch, DDP, FSDP, DeepSpeed, NCCL, Triton Inference Server,
NVIDIA NIM, vLLM, TensorRT-LLM, Python, MLOps, AI Infrastructure,
Kubernetes, Kubeflow, Ray, Argo Workflows, GPU Validation,
Performance Engineering, Benchmarking, RAG, Vector Databases,
LLM Inference, Distributed Training, AI Platform Engineering