New Roles · Engineering AI Builders
AI Data Engineer. The role, the market signal, and how to build it in your org.
Builds the data layer AI runs on: ingestion, feature stores, embeddings, retrieval. The plumbing that makes agents and models usable on real enterprise data.
In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.
64 postings · 57 distinct titles · from 264,613 real job postings · see the live data →
What the market calls it
AI Data EngineerML Data EngineerGenAI Data Engineer
Hiring this role in our corpus right now
PwC 7Thehartford 7Bosch 6Capitalone 5Soprasteria1 4
What the postings ask this role to do
1,237 tasks extracted from real AI Data Engineer job descriptions, classified Automate / Augment / Human-only. Only 3.2% can be fully automated: companies are hiring this role for the judgment, not the keystrokes.
Automate
- Catalog datasets that support applied ai retraining workflows.
- Normalize collected data for downstream ai and data workflows.
- Document processes, workflows, and configurations for reference and future improvements.
Augment
- Develop ai-driven systems to improve data capabilities.
- Collect data from multiple sources, including external llm providers.
- Stay current with emerging tools and methodologies in ai data engineering.
- Develop graph database solutions for complex data relationships supporting ai systems.
- Design scalable real-time data pipelines that support ongoing applied ai model training.
Human-only
- Mentor other members of the engineering community.
- Maintain clear communication channels across teams.
- Partner with ai r&d, applied ai, and data platform teams to ensure seamless data flow.
- Mentor junior team members and promote best practices, standards, and reusable patterns.
- Collaborate with cross-functional teams to integrate solutions into operational processes and systems.
How we build it in your org
From the market's version of this role to your version of it
1. Define workflow responsibilities
Start with the workflows this role supports: the outcomes, decisions, and handoffs it owns. Task Intelligence examines the tasks within that work and how people and agents can share responsibility.2. Define your version
Your team composes the job description for your org's variant of the role, grounded in those responsibilities and the task evidence rather than a copied template.3. Practise and assess readiness
Build on the domain and technical expertise your people already bring. Use relevant Simulations, GenAI Sandboxes, and Skill Validation Assessments to practise changed responsibilities and demonstrate capability. Revisit preparation when the work changes.Start with the work, not the org chart.
Define what this role will own, then connect those responsibilities to practice and assessment.