AI Infrastructure
Engineering
I design, build and operate the infrastructure that runs AI systems — from the GPU and inference layer up to the product interface. Local and on-prem by default, tuned for latency, VRAM and cost.
Infrastructure that runs AI — end to end
Most teams can call a model API. Fewer can run one well — on their own hardware, fast, cheap and reliable. I work across that whole stack: standing up model serving, squeezing latency and VRAM out of a GPU, building the MLOps and fine-tuning pipelines around it, and wiring in the agentic and retrieval layers that make it genuinely useful.
And because I'm also a design architect, the result isn't just a working backend — it ships with an interface and a developer experience people actually want to use.
What I Build
Stack
Python · TypeScript · llama.cpp · vLLM · PyTorch · Transformers · CUDA · Unsloth / QLoRA · Next.js · Node · Docker · Linux · RAG / vector search · evaluation harnesses
Frequently Asked Questions
What does an AI infrastructure engineer do?
I build and operate the systems that run AI in production — model serving and inference, GPU and VRAM optimization, MLOps pipelines, fine-tuning, evaluation, and the agentic and retrieval layers on top. Effectively everything between the hardware and the product interface.
Can the models run on-prem or fully offline?
Yes — local and on-prem is the default. I run open-weight models on your own hardware (or a private VPC) using llama.cpp and vLLM, so there is no dependency on a third-party API and no data leaves your environment.
How do you keep inference fast and cheap?
Quantization, KV-cache and flash-attention tuning, right-sizing the model to the task, batching, and measuring latency and cost per request end-to-end. The goal is the smallest, fastest setup that still hits the quality bar.
Do you work remotely?
Yes. I'm based in Plymouth, UK and work remotely with teams anywhere. Engagements are project-based or contract.
