Projects to Land an AI Job
12 in-depth AI engineering project briefs with staged milestones, evals, and deploy steps. MIT licensed.
A repo of 12 in-depth AI engineering project briefs, each with staged milestones, evals, and deploy steps, under MIT.
What it is
Projects to Land an AI Job is a curated set of twelve buildable AI and LLM portfolio projects, each written as a full brief. Every brief covers the problem, who would be impressed by it, the skills it proves, a suggested stack, staged milestones, an evaluation plan, a deployment path, stretch goals, and reference resources. The README argues that one production project with real evals beats several tutorial notebooks, and the briefs are built around that idea. The projects span retrieval-augmented generation, agent workflows, eval suites, an MCP server, a real-time voice agent, structured extraction, fine-tuning, LLMs built from scratch, search, guardrails, multimodal document AI, and LLMOps cost tracking. A bonus business-facing copilot is included too. The repository is maintained by Landed, a jobs platform that also promotes its own courses and services.
Key features
- Twelve project briefs, each with milestones, evals, and a deploy path
- Emphasis on named metrics, live deployments, and cost and latency numbers
- Difficulty ratings from beginner to advanced for each project
- Mapping of projects to specific AI role types such as RAG engineer or LLM engineer
- Presentation checklist for turning a project into a referral-ready portfolio piece
- Free course links on landed.jobs for ten of the twelve projects
Use cases
- Choosing one or two projects to build deeply for a targeted AI engineering role
- Using the eval and deployment expectations as a checklist for an existing portfolio
- Finding a structured path into RAG, agents, or fine-tuning with defined milestones
- Preparing to explain tradeoffs and evaluation results in interviews
Who it's for
Developers and career-changers preparing a portfolio for AI engineering, LLM engineering, or applied AI product roles. It suits people who already know some Python and want concrete build targets.
Getting started
The supplied README does not include a verbatim setup command, so no command is given here. Start by reading the project table, pick one or two briefs that match your target role, and open the linked brief for its milestones and eval requirements.
Alternatives
Related directory entries include r/LocalLLaMA, AI Engineering from Scratch (rohitg00), AMD Strix Halo Local LLM Guide, AI Agents in Depth, Claude How To, and Maths, CS & AI Compendium. These are listed as related entries only, not as endorsements from this README.
Drafted with AI from the project's public documentation; verify against the source. Written 1 Nov 2026.