Head of AI / VP of AI Recruiters

What this hire really comes down to

The Head of AI serves as the bridge between current research and commercial product delivery. This role focuses on defining the AI roadmap while building a team capable of deploying machine learning and LLM solutions into production. You are responsible for work through the tension between experimental flexibility and the operational discipline required for stable enterprise applications in changing market market.

Success hinges on the candidate's ability to articulate the value of AI initiatives to executive stakeholders while mentoring technical leads. You must determine when to build custom solutions and when to uses existing platforms. Balancing technical debt with innovation velocity is a constant challenge. We seek leaders who can foster a culture of rigorous scientific testing alongside reliable engineering practices.

What we check before you see anyone

• ML Production Experience

  We examine your record of leading teams through the full lifecycle of machine learning deployment. You must demonstrate how you managed data pipelines and model monitoring to ensure high performance in production. We look for specific examples where your team overcame drift or latency issues to deliver tangible business outcomes that align with the long term goals of the organization.

• LLM Deployment Strategy

  The candidate must have proven experience integrating large language models into existing workflows. We look for a deep understanding of fine tuning and prompt engineering techniques used to optimize model performance for specific use cases. You should describe how you evaluated model quality and addressed safety concerns such as hallucinations and data leakage while managing overall inference costs in production.

• Build Versus Buy

  We require a clear philosophy on when to develop custom AI infrastructure versus adopting third party services. Candidates must articulate their framework for evaluating cost, scalability, and security risks when choosing components. Successful answers describe past decisions involving infrastructure where technical tradeoffs were carefully weighed against time to market and the need for future control over intellectual property and performance.

• AI Governance Frameworks

  Safety and ethics are paramount in our AI development lifecycle. We evaluate your experience implementing governance structures that ensure fairness and compliance with emerging regulations. You must show how you track model provenance and handle security audits in sensitive environments. We look for leaders who prioritize risk management without stifling the creative exploration necessary for building advanced AI capabilities internally.

• Technical Leadership Depth

  We assess the candidate's ability to manage high functioning teams through periods of intense change. You must demonstrate how you attract and retain experienced machine learning talent. We look for evidence of your influence on engineering culture including how you encourage peer reviews and knowledge sharing to maintain high standards of code quality and scientific rigor across diverse technical disciplines.

• Strategic Alignment Skills

  You must prove your capacity to align AI research with overarching business objectives. We look for examples where you successfully communicated complex technical risks and possibilities to non technical leadership. Successful candidates show how they prioritize projects that provide the greatest uses while maintaining a balanced portfolio of immediate wins and longer term foundational research investments for the company.

Where we find them

Our sourcing strategy targets machine learning leaders who have work through the transition from research to commercial impact at innovative software companies. We look for individuals who are active in the broader research community and have a track record of speaking at technical conferences. By focusing on professionals with proven organizational leadership, we find candidates ready for high level technical management.

We also recruit from internal teams of established technology firms where AI capabilities are being heavily integrated into core products. Our search prioritizes those who have managed cross functional teams comprising researchers and infrastructure engineers. We engage leaders who possess a blend of strong technical depth in deep learning and the commercial acumen required to drive adoption and scale.

How we help you hire

1. Tell us who you need.

   Tell us about the job, your team and the experience you need.

2. We find and contact people

   We look for relevant experience and speak with people about the job and their interest.

3. You meet the strongest candidates

   We explain why each person is worth meeting and arrange introductions.

4. We help you complete the hire

   We help with interviews, feedback, the offer and arrangements for the start.

Your team handles technical interviews and any required credential checks.

Questions about this search

Where we fill this role

• SaaS and B2B Software
• Fintech, Payments and Insurtech
• Banking, Capital Markets and Trading
• Healthtech and Digital Health
• AI-Native Companies
• PE-Backed Software
• Enterprise and Fortune 1000
• Ecommerce, Retail and Marketplaces
• Govtech, Defense and Aerospace (Cleared)
• Cybersecurity Vendors
• Energy, Utilities and Climate Tech
• Consulting, MSPs and Systems Integrators
• Media, Gaming and Consumer Apps

• Head of AI, PE-Backed Software

Related roles and stacks

• AI, Machine Learning and Data Science
• Machine Learning Engineer
• AI Engineer
• MLOps Engineer
• Data Scientist
• Research Scientist / Applied Scientist
• Computer Vision Engineer
• Python
• C++
• GCP
• Kubernetes
• Databricks

Hiring guidance

Plan this hire

• Positions we recruit
• Team hiring guide
• Head of Data Science
• Platform Engineer, DevOps Engineer or SRE?
• Help desk tiers: where escalation should go
• Moving from an MSP to in-house IT
• Credential checks