Artificial Intelligence Engineer

Franklin Fitch; United States; Remote; USD 171,000 - 209,000; Full time

Job summary

Franklin Fitch is seeking an AI Infrastructure Engineer to join a highly technical team supporting cutting-edge AI, ML, and HPC platforms. You will shape and scale enterprise AI infrastructure, including GPU clusters and large-scale model workloads, collaborating with data scientists, researchers and engineering teams.

This fully remote US-based role offers the opportunity to work in a trusted consulting environment serving Fortune 500 clients, focusing on NVIDIA GPUs, SLURM, Docker and

Qualifications

• Experience within high-performance computing, research, or enterprise environments.
• Experience with Nvidia H200 Chips for HPC workloads.
• Hands-on experience with GPU infrastructure.
• Experience supporting AI/ML, LLM workloads.

Responsibilities

• Act as the LLM specialist on the HPC team.
• Administer GPU infrastructure and software components (CUDA, cuDNN, NCCL, NVIDIA GPU Operator).
• Work with NVIDIA H100/H200 hardware in production environments.
• Manage job scheduling through SLURM and other HPC schedulers.
• Containerise HPC workloads using Docker and Kubernetes.
• Design and maintain GPU clusters for AI/ML and HPC workloads.
• Fine-tune large models on multi-GPU clusters.
• Perform advanced performance tuning for AI/ML and LLM training workloads.

Skills

HPC experience

AI/ML expertise

Team collaboration

Tools

NVIDIA GPU

CUDA

Docker

Kubernetes

SLURM

Job description

AI Infrastructure Engineer | 190k | Fully Remote (US-based)

What's in It for You?

• Join a highly technical environment supporting cutting-edge AI, machine learning, and high-performance computing platforms.
• Play a key role in shaping and scaling enterprise AI infrastructure, including GPU clusters and large-scale model workloads.
• Work alongside data scientists, researchers, and engineering teams on innovative projects with real business impact.
• Work in a highly respected business consulting firm that works with Fortune 500 companies.

Responsibilities

• Act as the LLM specialist on the HPC team, working closely with 4 other HPC engineers.

HPC Responsibilities

• Administer NVIDIA GPU infrastructure and software components, including CUDA, cuDNN, NCCL, and the NVIDIA GPU Operator
• Work with H100/H200 hardware in production environments
• Manage job scheduling through SLURM and other HPC schedulers
• Containerise HPC workloads using Docker and Kubernetes

LLM Specialism

• Design and maintain GPU clusters for high‑demand AI/ML and HPC workloads
• Fine‑tune large models on multi‑GPU clusters
• Perform advanced performance tuning for AI/ML and LLM training workloads

Must-Have Requirements

• Experience within high-performance computing, research, or enterprise environments.
• Experience with Nvidia H200 Chips for HPC workloads
• Hands‑on experience with GPU infrastructure
• Experience supporting AI/ML, LLM workloads