There is a category of hire that is breaking HR teams right now. It is not the AI engineer. It is not the data scientist. It is the role that sits between the legal department, the product team, and the AI vendor — and the role that owns what happens when the AI makes a mistake.
These roles did not have names three years ago. Now they are the highest-leverage hires a company can make. And almost every job description written for them is wrong — wrong enough that the candidates who could actually do the job are scrolling past without applying.
The 20 Roles You Will Hire By 2027
FavHire Consulting has spent the last 18 months mapping the AI-era workforce across our client base — companies in legal, finance, healthcare, insurance, and consumer. Across those industries, the same 20 roles keep appearing. Some are new titles for new functions. Some are old titles for fundamentally new work. All of them are being hired right now, and almost all of them are being hired badly.
The 20 roles, in no particular order:
1. AI Governance Counsel — Legal ownership of AI risk, model contracts, vendor assessment, disclosure, and the regulatory perimeter. This is the fastest-growing legal role we are tracking.
2. Chief AI Officer — Not a Head of Data. A C-suite seat responsible for AI strategy, vendor portfolio, risk appetite, and the build/buy/partner roadmap.
3. Head of AI Strategy — Reports to the CEO or CAIO. Translates AI capability into business line plans. Owns the AI initiative backlog.
4. AI Risk & Compliance Lead — Bridges legal, model risk, and the regulators. Increasingly required by examiners in regulated industries.
5. AI Vendor Manager — Owns the 5 to 15 AI vendor relationships that the modern enterprise now carries.
6. Prompt Engineering Lead — Increasingly renamed "AI Operations Lead." Owns the production prompts, the evals, and the iteration loop.
7. AI Operations Manager — Runs the day-to-day of AI systems in production: monitoring, incident response, cost management.
8. AI Quality Assurance Lead — Tests model behavior, hallucination rates, regression across releases. This is QA for systems that do not behave deterministically.
9. AI Training & Enablement Manager — Builds the internal curriculum that turns 500 employees into a workforce that can use AI safely and effectively.
10. AI Ethics & Responsible Use Lead — The public-facing conscience of the AI program. Increasingly demanded by enterprise buyers and by the press.
11. Agentic Workflow Designer — Designs the handoffs between human workers and AI agents. Brand new discipline.
12. AI Integration Engineer — Takes vendor and internal models from demo to production. Distinct from ML engineer.
13. AI Product Manager — Owns AI-powered product surfaces. Different skill set from a traditional PM.
14. Human-AI Collaboration Lead — HR-side counterpart to the technical roles. Owns change management, job redesign, and the retention plan.
15. AI Data Governance Lead — Owns what data the models can see, retention, lineage, and the policy layer.
16. AI Security Engineer — Defends against prompt injection, model exfiltration, and the new class of attacks on AI systems.
17. Model Risk Officer — Required in financial services; spreading to insurance and healthcare. Owns model validation and the SR 11-7-style documentation stack.
18. AI Procurement Specialist — Knows how to buy AI. Contracts for non-deterministic systems are different from SaaS contracts.
19. AI Communications Lead — Crafts the internal and external narrative of the AI program. Often the difference between adoption and revolt.
20. AI Transformation Director — The program-management layer that holds 15 of the other 19 together.
Why the Job Description Is the First Filter
Here is what happens in the market right now. A company posts an "AI Governance Counsel" role. The job description is a copy-paste of a deputy general counsel JD from 2019 with "and AI" added at the bottom. The candidates who actually do AI governance work read the JD, conclude the company does not know what the role is, and do not apply. The candidates who do apply are deputy general counsels from 2019. The company hires one. Six months later, the company realizes the hire has never read a model card, never negotiated a model license, and does not know what an eval is. The role is re-posted. Cycle repeats.
The candidates who can do these jobs are rare and they are selective. They read job descriptions carefully because they have been burned by companies that did not understand the role. Your JD is the first interview — and most companies are failing it.
What a Modern AI-Era JD Contains
We have written and rewritten these JDs for clients across the last 18 months. The ones that attract the right candidates share seven components:
1. The honest scope. "You will own the AI governance framework for a company deploying 14 AI systems across 3 regulated business lines" beats "You will assist with AI-related legal matters."
2. The actual systems in production. List the vendor categories, the deployment stage, and the regulatory exposure. Serious candidates want to know whether they are walking into a mature program or a greenfield.
3. The reporting line and the budget. These roles fail when they report four layers down with no budget. Name the reporting line in the JD.
4. The first 90 days. What does success look like at day 90? Candidates want to know whether the role is set up to win or set up to be scapegoated.
5. The team. Who do they inherit, who do they get to hire, and who are the internal partners. This signals whether the company has thought about the operating model.
6. The actual qualifications. Real AI Governance Counsel roles require some mix of: privacy law, vendor negotiation, model risk documentation, and enough technical literacy to read an eval report. They do not require "10 years of general commercial litigation experience." If your JD lists the latter and not the former, you will get the wrong applicants.
7. The compensation band and the level. AI-era candidates are not applying to roles with hidden comp and ambiguous levels. The market is too tight and too informed.
An Excerpt From The AI-Ready Hiring Kit, Chapter 3
"Job description template for AI Governance Counsel. Boilerplate to remove: '10+ years of relevant legal experience.' Replace with: '5+ years legal experience with at least 2 years in privacy, fintech, healthtech, or a comparably regulated industry. Demonstrated experience reviewing or negotiating contracts for AI/ML systems, SaaS, or data licensing. Working fluency with the major AI regulatory frameworks (EU AI Act, US state AI laws, sector guidance from OCC/FDA/FCC as applicable). Comfort reading eval reports and technical documentation written for engineers is required; we will test for this in the interview.'"
"That paragraph changes who applies. It also changes who does not apply — which is the point."
Get the Full Kit for $2
This is one of 20 templates and a fraction of the framework. The AI-Ready Hiring Kit contains all 20 complete job description templates with comp guidance, the six competencies of AI readiness and the interview questions that expose each one, scoring rubrics with the three-probe rule that defeats coached answers, reference check scripts that verify claims, offer letter language for AI-era roles, work-sample exercises, and per-role 90-day success profiles.
Two dollars. One bad hire costs six figures. Get The AI-Ready Hiring Kit at FavHire.
The Bottom Line
The 20 roles above are coming whether you plan for them or not. The companies that hire them well in the next 18 months will compound that advantage. The companies that hire them badly — or not at all — will spend 2027 cleaning up avoidable failures.