Other

Senior Engineer AI Workload & Infrastructure Validation

VST Consulting, IncPlano, TX🇺🇸United StatesPosted Oct 20, 2026

Why This Role Stands Out

This hybrid role offers a fantastic opportunity to shape the future of AI infrastructure by validating and optimizing cutting-edge GPU technologies for enterprise clients. You'll thrive here if you have a passion for ML engineering, MLOps, and performance tuning, and are eager to develop deep expertise in distributed training and inference serving. Don't miss out on the chance to join a growing practice with significant impact!

Quick Overview

Seniority

Mid Senior

Work mode

Hybrid

Location

Plano, TX, United States

Posted

21 hours ago

MLOpsLLMPyTorchPythonKubernetesRAG

Job Description

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)

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