Data engineering AI Jobs
Pipelines, lakehouses, feature stores. Training datasets and inference logging at AI volume.
Every role on this page is from a company we recognise as building AI as a primary product. No bolt-on AI roles, no third-party scrapers, no padding.
Live roles
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New to AI work? Start with these.
Six pieces of orientation. Most AI-company job specs assume you've done this kind of hands-on work already. If you haven't, an afternoon with one of these is the cheapest way to close the gap.
Claude Desktop, from zero.
The agentic-AI assistant most of the people you'd be working alongside use every day. Install, configure, first useful prompts.
What MCPs areThe best MCPs for Claude Desktop.
MCP servers extend an AI assistant with tools and data. The catalogue most teams use. Useful technical context for any AI-engineering role.
Code with AIClaude Code, the complete beginners' guide.
The CLI for AI-paired development. Required reading if you're applying for any engineering role that mentions agents, or any role full stop.
Run a local modelHow to set up LM Studio.
Running a model on your own machine teaches you more about how AI products work in three hours than a year of using ChatGPT will.
The hardware realityBeginner's guide to AI hardware.
What the infrastructure under the model actually looks like. Useful context for infrastructure, applied-AI and hardware roles.
Browse the stackMCP catalogue.
Eleven MCP servers Houtini maintains or recommends. Each detail page describes a real piece of working AI infrastructure.