Your first senior AI leader
Retained search tests candidates on evidence of production delivery and governance, not on vocabulary, which is where most AI leadership hires go wrong.
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Practice
Generative AI
Leaders and builders for companies putting generative AI into products and operations, where the hard part is turning pilots into governed, measurable production systems.
Market view
What we are seeing
Most organizations have run generative AI pilots. Far fewer have shipped governed systems that change how work gets done. We appoint the leaders who own that shift, staff the engineers and applied scientists who build it, and stand up delivery teams that take use cases from proof of concept to production.
Pilot to productionAI governanceAgents and automationAI talent strategy
Roles we place
Leadership
- Chief AI Officer
- Head of AI and Machine Learning
- Vice President of AI Product
- Head of Responsible AI and AI Governance
- Head of AI Transformation
Technology
- Applied AI and LLM engineers
- ML platform and MLOps engineers
- Data engineers for AI readiness
- AI security and red team specialists
Specialist
- AI product managers
- Model evaluation and quality specialists
- Prompt and conversation designers
- AI risk and compliance analysts
Industry context
What is shaping this practice
- Boards and executive teams are asking for measurable returns from generative AI, which moves the conversation from experimentation to accountable delivery.
- Model capability is changing quickly, so leaders need the judgment to choose between building, buying and partnering without locking the business into one approach.
- Regulation, intellectual property and data privacy expectations around AI are maturing across the US, Europe and India, and governance has to be designed in from the start.
- Demand for applied AI engineers, ML platform engineers and AI product leaders far exceeds supply, and compensation for proven talent has risen sharply.
Challenges
Where the pressure sits
- Few leaders have taken generative AI from pilot to governed production at enterprise scale
- AI engineering talent is contested between technology companies, startups and enterprises
- Use cases stall without data readiness, evaluation frameworks and clear ownership in the business
- Governance, risk and security teams are often brought in too late to shape the design
- Titles vary widely, which makes it hard to benchmark roles and compensation accurately
Talent needs
Leadership and technology
Leadership
- Chief AI Officer and Head of AI leadership with enterprise delivery experience
- AI product leaders who can connect use cases to measurable business outcomes
- Heads of ML platform and AI engineering
- AI governance, risk and responsible AI leadership
- Transformation leaders who can change workflows, not just deploy tools
Technology
- Applied AI and LLM engineers for retrieval, agents and workflow automation
- ML platform, MLOps and model evaluation engineering
- Data engineering to make enterprise knowledge usable by AI systems
- AI security, guardrails and red teaming capability
- Integration engineers who connect AI services to core business systems
Engagement
Ways to start
A use case that has stalled after the pilot
A scoped engagement defines the evaluation criteria, data work and ownership needed to take it into governed production.
Explore solutionEngineers to accelerate an active AI program
Specialist AI engineers can be added for a defined phase without waiting on a full permanent hiring cycle.
Explore solutionProcess considerations
- Candidate assessment for AI roles focuses on systems shipped, evaluation practice and governance decisions, with evidence requested rather than assumed.
- Compensation benchmarking accounts for the wide variation in AI titles and the competition from technology companies.
- AI delivery work is scoped with data access, security and responsible AI requirements agreed before build begins.
Questions
Frequently asked questions
How do you assess whether an AI leader has real production experience?
We ask for evidence: systems shipped, how they were evaluated, what governance decisions were made and what changed in the business as a result. Assessment is structured around that evidence rather than titles or vocabulary.
Can you help us decide whether we need a Chief AI Officer at all?
Yes. Leadership advisory can help you define where AI leadership should sit, what it should own and whether the need is a single executive, a team or a delivery partner first.
Do you build AI solutions or only hire for them?
Both. Our AI and technology solutions team scopes and delivers specific use cases, and our search and staffing services build the internal team that will own them over time.
How do you handle data privacy and intellectual property in AI work?
Data access, privacy, intellectual property and security requirements are agreed with you before any build begins and documented as part of the engagement scope.
How we help
Solutions applied in this practice
Search, staffing, delivery and capability work, sequenced to the problem rather than sold as a bundle.
Retained Executive Search
Board and C-suite appointments run as a disciplined research process.
Staffing Solutions
Contract, contract to hire and direct hire technology and engineering talent.
GCC Services
Set up a capability center in India, run it while it matures, then hand it over.
Leadership Advisory and Human Capital Strategy
Decide the shape of the leadership team before you decide who fills it.
SOW and Managed Teams
Buy an outcome and a team that owns it, not a stack of individual requisitions.
AI and Technology Solutions
Leadership, talent and delivery teams for AI and technology initiatives.
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Hiring in generative ai?
Start with a market conversation. We will share what the role looks like in the current market before anyone commits.