Hirra
Hire Talent

Expertise

PaymentsFintechEngineeringAI & DataRisk & ComplianceProduct & Commercial

Services

Specialist RecruitmentExecutive SearchEmbedded Recruitment

Company

Jobs Insights Locations About Contact
Hire Talent Join the Talent Network

Expertise

AI & data recruitment

AI, ML and data talent for companies putting models into production — including fraud, risk and financial decisioning.

Submit a Role Book a Hiring Call

The gap in AI hiring is rarely modelling ability. It is the number of people who have taken a model into production, monitored its drift, and defended its decisions to a risk committee or a regulator.

We recruit across AI infrastructure, applied AI and LLM engineering, and the data platform work that determines whether any of it functions.

What we understand about this market

Published so you can assess our domain knowledge before the first call, rather than after it.

  • The difference between research, applied AI and ML platform engineering, and which one a brief actually needs
  • Production ML concerns: feature stores, training/serving skew, drift monitoring, evaluation
  • LLM engineering in practice — retrieval, evaluation, guardrails, latency and cost control
  • Fraud and risk decisioning, model explainability and adverse-action requirements
  • Model governance and regulatory expectations on automated decision-making in financial services
  • Data platform foundations: pipelines, warehousing, lineage, data quality and contracts

What we screen for

The signals that separate candidates who have done the job from candidates who can describe it.

Has anything they built reached production?

We ask for a specific model or system in live use, its evaluation approach, and what degraded over time. Notebook-stage work is easy to mistake for production experience on paper.

Evaluation discipline

For LLM roles we probe how they measured quality beyond vibes — datasets, offline evaluation, human review loops, and regression detection on prompt or model changes.

Explainability where it is required

In credit, fraud and financial decisioning, a model that cannot be explained cannot be deployed. We screen for candidates who have worked within that constraint.

Where these hires go wrong

  • Writing a research-scientist brief when the real need is an engineer who can productionise and operate models.
  • Hiring AI talent before the data platform can support them, which reliably produces early attrition.
  • Screening LLM engineers on modelling theory rather than evaluation, retrieval quality and cost control.

Frequently asked

Do you recruit AI talent outside financial services?
Yes. We recruit for AI infrastructure, enterprise AI and data-intensive products generally. Our strongest coverage is where AI meets finance — fraud, risk, decisioning and compliance automation.
What is the difference between an ML engineer and an applied AI engineer in your searches?
We use ML engineer for candidates who train, evaluate and deploy models, and applied AI engineer for candidates who build products on top of existing models — retrieval, orchestration, evaluation and guardrails. Briefs frequently conflate the two, and clarifying it early is usually the single biggest improvement to the shortlist.

Hiring a role that generalist recruiters keep getting wrong?

Send us the brief. We will tell you honestly whether we are the right people for it, what the market looks like, and what it will take to hire.

Submit a Role Book a Hiring Call