Mandate Area
AI Engineering & ML Leadership
Engaged executive search for Head of AI Research, VP ML Engineering, Heads of MLOps, Applied AI Directors, and succession-grade AI leaders inside scale-stage and mature operators. Engaged mandates only. The leader who bridges research and production - who has taken models from notebook to systems customers actually rely on - is the hardest hire in technology. It is the hire we are built for.
Engaged mandate · Partner-led · Confidential · Reply within 24 hours
Mandate Context
Built For
Boards, CEOs, CTOs and Chief AI Officers hiring the leader who has to take models from notebook to production at enterprise scale.
The bottleneck in enterprise AI is no longer compute or capital - it is the small population of leaders who can hold a research conversation in the morning and a production incident review in the afternoon. We work the global talent pool that lives between research labs, applied AI teams and platform engineering, and we present three names you can defend to the board.
- Typical Mandates
- Head of AI Research · VP ML Engineering · Head of MLOps · Head of Applied AI · Chief Scientist
- Sector Focus
- Frontier model operators, scale-stage AI-native, regulated enterprises productising AI
- Geography
- Singapore · Amsterdam · Dubai · cross-border APAC and EMEA
- Engagement Model
- Engaged search · NDA available · No contingency
The Market
AI talent scarcity is structural, not cyclical
Every enterprise AI initiative requires the same scarce resource: leaders who can translate research into production systems that customers rely on. The 87% failure rate of AI projects leaving prototype is not a technology problem. It is a leadership problem - the gap between teams that can build models and organisations that can deploy them.
The talent pool for genuine research-to-production leaders is measured in hundreds globally, not thousands. OpenAI, Google DeepMind, and every well-funded startup compete for the same small set of candidates. Meanwhile, the gap between AI strategy and delivery capacity widens with every quarter of unfilled leadership roles. The bottleneck is not the technology. It is the human infrastructure required to deploy it.
This is why AI engineering leadership - not capital, not compute, not model architecture - is the decisive variable. The organisations that place the right Head of AI Research six months earlier ship products their competitors are still prototyping.
"AI talent scarcity is structural. The organisations that treat it as a temporary market condition will still be looking for their first ML platform lead in 2027."
The Precision
Three signals that distinguish exceptional AI research leaders
The Head of AI Research you need isn't looking for a job. She's running a lab, publishing in NeurIPS, and advising three startups. She would only move for a problem she can't ignore. Identifying whether someone has the right capability is hard enough - identifying whether they have the right conviction requires a different kind of precision.
The Production Bridge
Research excellence and engineering discipline are different skills. Most AI researchers optimise for publication velocity, not production reliability. The signal we look for is someone who's done both - who understands statistical significance and distributed systems performance. Who can speak the language of researchers and platform engineers without losing credibility in either room.
IMPACT: IntegrationThe Network Effect
Your Head of AI Research needs to hire ten researchers. She's competing with OpenAI, Google DeepMind, and every well-funded startup for the same small pool of talent. Unless she has research network credibility - a name that opens doors - those researchers won't return her calls. Compensation alone won't attract them. The research vision might, if she can articulate it credibly. We assess whether a candidate can actually build the team, not just lead it.
IMPACT: Conviction DepthResponsible Velocity
Your AI system will make decisions that affect people's lives. Your regulators will ask how it works. “We used a black box model because it performed best” isn't an acceptable answer anymore. The leaders we look for understand AI safety, algorithmic fairness, and explainability as engineering disciplines - embedded from day one, not retrofitted after the regulator calls. The emergence of the AI Safety Officer role reflects the regulatory reality these leaders have already internalised.
IMPACT: Accountability ArchitectureHow IMPACT Calibrates
What we weight differently for AI engineering mandates
The IMPACT Framework's seven diagnostic questions apply to every search. For AI engineering mandates, three dimensions carry disproportionate weight - because the failure modes are research-specific and the cost of misalignment is measured in years of lost product velocity.
Integration - The ninety-day window is existential for AI leaders. A Head of Research who recruits exclusively from her former lab alienates the existing engineering team. A VP ML Engineering who restructures the data pipeline before understanding the model architecture creates compounding technical debt. We probe whether candidates diagnose organisational context before prescribing solutions.
Conviction Depth - Research leadership requires defending technical bets against commercial pressure. The leader who pivots research direction every quarter because the CEO read an article about the latest model architecture will never ship anything meaningful. We assess whether candidates have the intellectual conviction to hold a research vision - and the political skill to protect their team from organisational noise.
Pattern Recognition Under Novelty - AI moves faster than any other technology domain. The model that defined state-of-the-art six months ago is now a benchmark to beat. We assess whether candidates can unlearn outdated assumptions as quickly as they learn new ones - a form of intellectual agility that publication records alone cannot reveal.
Who We Find
Leaders who bridge research and reality
These are the AI engineering and MLOps profile types our partners have placed - bridging research capability and production reality.
Head of AI Business Development
Bridges research capability and commercial opportunity. Identifies enterprise use cases, builds strategic partnerships, and translates technical differentiation into revenue - turns AI investment into market traction.
VP Pre-Sales Engineering
Technical sales leadership for AI products and platforms. Builds the demo-to-deployment bridge enterprise buyers require. Scopes engagements that engineering can actually deliver.
VP / Head of ML Engineering
Built machine learning infrastructure at scale. Understands distributed training, model serving, feature stores, and monitoring for model drift. The leader who turns research into production.
Head of MLOps
Understands that ML systems are fundamentally different from traditional software. Builds infrastructure for data quality, model versioning, experiment tracking, and deployment automation.
Head of Applied AI
Sits between research and product. Takes research breakthroughs and makes them useful. Understands customer problems and technical capabilities equally well.
Head of AI Product Marketing
Positions AI capabilities for technical and non-technical buyers alike. Crafts narratives around model performance, safety, and competitive differentiation that translate lab results into market demand.
By Market
AI Engineering Search Across Three Corridors
Singapore: AI Infrastructure Leadership
NUS, SUTD, and hyperscaler R&D labs create a dense AI talent ecosystem with aggressive commercial adoption.
→Amsterdam: AI Engineering Search
TU Delft, Eindhoven, and the BeNeLux corridor supply deep-tech AI talent with European governance fluency.
→Dubai: AI Infrastructure Search
Sovereign AI investment and GCC compute ambition creating new demand for applied AI leadership.
→Context
Related Services & Perspectives
Executive Hiring Lab
Transfer our search methodology to your internal team. One day. Permanent capability.
→AI Capability Assessment
Map your leadership against your actual AI challenges before the search begins.
→Our Practice & IMPACT Methodology
Seven questions where others ask seventy. The framework behind every mandate.
→Track Record
Mandate case studies across AI infrastructure and engineering placements.
→The AI Execution Gap
Why 87% of AI projects never leave the prototype phase.
→From Compression to Orchestration
The high-agency leadership gap in enterprise AI.
→— FREQUENTLY ASKED
Common Questions
Know What You're Missing Before You Brief.
The IMPACT Framework. Six dimensions that distinguish leaders who execute from those who present well. Used in every Sercxi assessment. Download the diagnostic.
No sequence. No newsletter. One document.
Research Leadership Doesn't Get Found on LinkedIn.
Placements for AI Engineering and Research leaders who build, not just deploy.
Confidential · 30 minutes · Partner-led · No obligation
Three candidates, every one we would hire ourselves.
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