How it works
How it works
This index replaces guesswork with structured discovery. Employers define what they need, engineers prove what they can build, and the pages connect them through capability — not keywords.
For employers
Find the right AI engineer
Define your need
Describe the project or role. The taxonomy translates “we need an AI person” into prompting, retrieval, agents, evaluation, guardrails, or production.
No more guessing whether the title is prompt engineer or ML engineer. Requirements map to capabilities you can filter.
Discover matching engineers
Browse Discover with capability filters. Every published profile shows a skill list and evidence links.
Combine slugs, search by name or headline, and keep a shortlist on Convex. Nothing is ranked.
Evaluate with confidence
Open the profile: headline, summary, capabilities with definitions, and the evidence register.
Compare shortlisted people on standardized fields. Introductions still go by email.
Connect
Write to the engineer via the listed mailto, or send a concierge brief if you want a hand-picked shortlist.
No recruiter inbox and no self-serve checkout on these pages.
For engineers
Showcase what you actually build
Build your profile
A structured showcase: headline, taxonomy slugs, location, availability. The /build wizard writes the draft to Convex.
Go beyond a generic resume. Name prompting, agents, RAG, evaluation, guardrails, production, or fine-tuning with evidence.
Add proof of work
Attach case studies, repos, demos, talks. Each link has a kind and a host.
Employers see evidence aligned to the capability they filtered on.
Get discovered
Once published, the profile enters the static index. Employers find you by capability, not keyword scraping.
Sign in to edit and publish. Mail us if you lose the account and need a row updated or removed.
Taxonomy
The AI engineering taxonomy
Design, optimize, and manage prompts. Chain-of-thought, few-shot design, and prompt versioning.
Tool use, planning loops, multi-agent coordination, and state management.
Retrieval-augmented generation, embeddings, chunking, and context-window discipline.
Eval frameworks, test suites, model quality, and regression testing for AI systems.
Content filters, output validation, red-teaming, and compliance-aware deployment.
LLMOps, inference, caching, monitoring, cost, and reliability at scale.
Ready to look
Whether you are hiring or listing, the door is Discover or the profile wizard.