Kyle Arriola, Harnham’s AVP of Data & AI Talent Acquisition, recently spoke with Simon Clarke, CEO of Harnham Group, about how companies should approach choosing the right AI and data recruitment partner in the US.
The conversation focused on common challenges hiring teams encounter today, including, unclear role definitions, inconsistent job titles, and difficulty assessing whether candidates can deliver in real environments.
In this conversation, they cover:
- What separates strong AI recruiters from generalist firms
• Why technical understanding matters in AI and data hiring
• Common red flags when working with recruitment agencies
• What to look for in a long-term recruitment partner
Watch the full episode below:
Why CVs aren’t enough in AI hiring
CVs don’t show how someone will perform in the role
Titles are inconsistent, tech stacks change quickly, and two candidates with the same experience can deliver very different outcomes once they’re in the role.
The difference comes from understanding how that experience translates into real work.
“A good AI recruiter understands the difference between skills on paper and what actually works in production. They understand the tooling, the trade-offs, and how teams are structured. They can challenge hiring managers when something doesn’t make sense and speak credibly with technical teams. It’s not just about selling a job.”
AI roles aren’t standardised and can’t be assessed through keywords
One of the biggest challenges in AI hiring is the lack of standardisation.
The same title can mean different responsibilities depending on the company, industry, or team structure. Two candidates with the same role on paper can be doing completely different work.
This is where generalist recruitment models fall short.
“Generalist recruiters tend to focus on speed and volume, but they’re often relying on keyword matching. In AI, that’s risky. Titles vary, tech stacks move quickly, and two ML engineers can be doing completely different jobs.”
Many firms still approach AI hiring through keyword matching, filtering CVs by titles, tools, and experience. In this market, that doesn’t hold up.
Without a clear understanding of what the role requires, hiring processes start misaligned from the outset.
“The more focused a recruiter is in this space, the more they understand what clients actually need. They become more technical, ask better questions, and align experience properly rather than just sending CVs.”
Specialist recruiters guide the hiring process with market insight
Strong recruiters don’t just take requirements at face value.
They bring context from the market: shaping role scope, aligning expectations, and helping hiring teams understand what’s realistic. That includes advising on salary, availability, and how the role should be positioned to attract the right talent.
This is what keeps hiring aligned with the realities of the market.
“The firms that work best are the ones that live in this space every day, understand the roles at a technical level, and have real networks—not just resumes. AI and data hiring in the US is very different from general tech recruiting.”
Red flags when choosing an AI recruitment agency
Speed is often positioned as a strength in recruitment. On its own, it’s not.
In AI hiring, fast delivery without depth usually leads to poor screening and weak alignment to the role.
“If a firm is promising speed without talking about quality… that’s a huge warning sign.”
That usually shows up in a few ways:
- CVs sent quickly without clear rationale
- Little discussion of the talent market or role requirements
- No pushback on unrealistic expectations
The focus needs to be on getting the right person, not just getting someone quickly.
AI hiring requires a long-term partner with proven delivery
“A firm should treat the relationship as a partnership. They’re not just here as vendors or to fill openings… they’re acting as advisors.”
AI hiring isn’t a one-off process. Roles evolve, requirements change, and hiring plans shift as teams grow. The relationship between hiring teams and recruiters needs to reflect that.
That only works when there’s a track record behind it.
In a complex market such as tech, past delivery is one of the clearest signals of capability. It gives hiring teams a clearer signal of whether a partner can deliver.
“A firm that is able to provide case studies on previous projects they’ve worked with, with a large clientele…I think that’s a huge green flag.”
That combination, partnership and proven delivery, is what supports consistent hiring outcomes.
Building a strong AI hiring strategy
AI hiring doesn’t follow the same rules as broader tech recruitment.
Roles aren’t standardised. Titles don’t tell the full story. And relying on CVs or keyword matching leads to poor alignment from the start.
Strong hiring comes from understanding context, challenging assumptions, and making informed decisions as requirements evolve.
If you’re hiring AI and data talent, focus on the fundamentals:
- Are your roles clearly defined beyond titles?
- Are you getting realistic insight into the talent market?
- Are your recruitment partners advising, or just delivering CVs?
If you’re looking to sense-check your approach or discuss your hiring plans:
Speak to a Harnham specialist recruiter
For a broader view of the US AI hiring market, we’ve put together a practical guide covering salary benchmarks, role trends, and hiring considerations.