Data & ML, fractional.
Engineers who make LLM systems measurable — schema-first extraction, data infrastructure, evals, and pipelines that survive contact with production.
When to bring one in
- Extracting structured data from messy documents at scale
- Building eval suites so model changes stop being vibes
- Production data pipelines that feed AI features reliably
- Engagements
- Pipeline build (2–6 weeks) · Eval harness sprint · Fractional data engineer
- Vetting
- Public proof of shipped AI work — repos, production systems, measured outcomes. Reviewed by humans.
- Start
- Shortlist in days, not months. Start scoped, expand on results.
- Network
- 3 data & ml listed with public profiles.
In the network
Profiles link to public proof — showcases, repos, and source notes. Matching always runs through a brief so we can pair the right person with your exact problem shape.
@jason-liu
Instructor creator — structured outputs, evals, and production LLM workflows
view profile → E Edward Lawless@edward-lawless
AI systems builder — game-AI, healthcare data infra, research automation
5 public showcases → P Pydantic Team@pydantic-team
Docs-first workflow source — typed agents, structured outputs, and eval boundaries
view profile →Need an AI Data Engineer?
Send a short brief describing the outcome you need. We'll reply with a proof-backed shortlist — or tell you honestly if we don't have the right match.
Other roles
AI Engineers
Agent systems, LLM features, and production AI infrastructure.
6 in network view → ~/hire/ai-productAI Product Leaders
Product managers and founders who run AI-native product orgs.
2 in network view → ~/hire/ai-engineering-leadershipEngineering Leaders
Leaders who have rewired real engineering orgs around AI.
5 in network view → ~/hire/ai-ops-automationAutomation & Ops
Workflow automation across n8n, agents, and internal tooling.
4 in network view → ~/hire/ai-marketingMarketing & Content
Growth and content operators with AI-native production systems.
2 in network view →