Applied AI, Foundation Models, ML Platforms & Data Leadership
AI & Data Science
Executive Search
60+ AI & Data Leadership Placements — with an average 67 Days time-to-placement and a 12-month candidate guarantee.
60+
AI & Data Leadership Placements
67 Days
Avg. Time-to-Placement
90%
Offer Acceptance Rate
12 Months
Candidate Guarantee
Specialisation withinTechnology & Digital·Powering the Digital Economy
AI has moved from an R&D curiosity to a board-level operating priority in 18 months. Indian enterprises are rebuilding core workflows around large language models; AI-first startups are shipping production foundation-model-derived products; and GCCs are housing global AI platform teams that design inference infrastructure, MLOps tooling, and responsible-AI frameworks for parent companies. Leadership hiring has compressed from 'hire an ML lead when we need one' to 'stand up a Chief AI Officer and an AI operating committee'. The candidate pool is thin, global, and in active demand; retained closed-network search is the only realistic channel.
Is This Your Situation?
If any of these sound familiar, you're speaking to the right practice.
→Enterprise (BFSI, healthcare, or consumer) standing up a Chief AI Officer role and an AI operating committee for the first time.
→AI-first SaaS company hiring a VP Applied AI and a Head of ML Platform as twin hires to institutionalise the research-to-production pipeline.
→GCC site hiring an AI Site Lead to run a global AI platform team with dual-reporting lines to global AI heads and Indian country MDs.
→BFSI enterprise hiring a Head of Model Risk / AI Governance to operate under RBI MRMG guidelines and internal board AI governance frameworks.
Our AI & Data Science Track Record
Situation:
A top-5 Indian private bank's board mandated the appointment of a Chief AI Officer to unify enterprise AI strategy across credit, fraud, customer experience, and operations — reporting jointly to the CEO and the board risk committee. Candidates needed board-level governance fluency alongside deep applied AI credibility.
Outcome:
Placed a CAIO who had previously run applied AI at a global bank's central AI function and held a PhD in ML. The search ran 92 days, closed a slate of six, and culminated in three board-interview rounds. The incoming CAIO built the bank's model-risk framework and AI operating committee within the first two quarters.
Situation:
A Series B legal AI SaaS franchise with $15M ARR needed twin hires to institutionalise its AI engineering — a VP Applied AI to own evaluation, RAG systems, and agentic workflows, and a Head of ML Platform to own training, serving, and MLOps.
Outcome:
Closed both hires within 70 days. VP Applied AI placed from a foundation-model lab with prior production deployments of agentic systems; Head of ML Platform placed from a hyperscaler AI infra team. Both hires closed on USD-denominated packages with dual-jurisdiction equity structures.
Situation:
A GCC housing the global AI platform team for a top-10 global bank needed a Head of AI Governance to own model-risk, responsible AI, and regulatory-disclosure processes across the parent's global AI portfolio — operating under EU AI Act, GDPR, and US state-level frameworks.
Outcome:
Placed a leader who had previously built a model-risk function at a major Indian private bank and had subsequently advised global consultancies on AI governance. The role closed on a USD-anchored package at parity with London and Singapore AI governance bands.
All client details anonymised. Specific mandates available for reference under NDA upon request.
Our AI & Data Science Practice
AI has moved from an R&D curiosity to a board-level operating priority in 18 months. Indian enterprises are rebuilding core workflows around large language models; AI-first startups are shipping production foundation-model-derived products; and GCCs are housing global AI platform teams that design inference infrastructure, MLOps tooling, and responsible-AI frameworks for parent companies. Leadership hiring has compressed from 'hire an ML lead when we need one' to 'stand up a Chief AI Officer and an AI operating committee'. The candidate pool is thin, global, and in active demand; retained closed-network search is the only realistic channel.
We place AI, data science, and ML-infrastructure leaders across foundation-model research labs, applied-AI SaaS companies, enterprise AI platforms, domain-specific AI verticals (legal AI, healthcare AI, BFSI AI), and GCCs building AI centres for global parents. Engagements include Chief AI Officer searches, Heads of Applied Research, VPs of ML Engineering and MLOps, Chief Data Officers, and senior individual contributors — principal research scientists, distinguished engineers, and AI architects — where a single hire can shift a company's technical trajectory.
Our distinction is that we read the AI market as a set of specialised sub-disciplines rather than a monolithic 'AI leader' search. Research-oriented roles (foundation model training, post-training, multimodal architectures) draw from a narrow global pool of academic-adjacent operators. Applied AI leadership (fine-tuning, RAG systems, evaluation, guardrails) draws from a different pool that has lived inside production AI deployments. ML platform and infrastructure leadership (training infra, serving, feature stores, MLOps) is closer to infrastructure engineering than to data science. CDOs and data governance leaders sit yet elsewhere. We match each mandate to the specific sub-discipline and construct a slate that reflects it.
As a specialist CTO mandates for AI-first companies, our practice also covers CIO and enterprise AI leadership, our practice also covers CPO and AI-product leadership, and as a source for Technology & Digital practice overview.
The AI & Data Science Landscape Today
India has emerged as a material node in the global AI operating stack. Foundation-model research is now done out of India by global labs (DeepMind, Meta AI, Microsoft Research) and by a growing set of India-headquartered research groups. Applied AI SaaS is a fast-growing sub-category — legal AI, customer-support AI, marketing AI, sales AI, and healthcare AI companies built on top of GPT-4-class and Claude-class models. GCCs have elevated AI talent into C-suite-adjacent roles; multiple global banks, consumer, and tech companies now run AI platform and responsible-AI teams out of Bengaluru and Hyderabad. Public-cloud AI compute has become a structural boardroom topic — training cluster allocation, inference cost curves, and multi-cloud GPU strategy now sit on CTO and CIO agendas. Leadership compensation has bifurcated: research scientists and applied AI leaders with credible published work or production LLM deployments command USD-benchmarked comp with equity at the upper end of SaaS norms; general-purpose ML leaders without that specificity compete in a broader, more price-sensitive pool. Responsible AI, AI governance, and model-risk management have emerged as dedicated leadership roles — particularly in BFSI and healthcare — and are now being filled out of a combination of traditional risk/compliance talent and applied AI research backgrounds.
Key Leadership Challenges in AI & Data Science
Hiring a Chief AI Officer or Head of AI who can bridge research credibility, applied product delivery, and board-level AI governance — a combination that is rare in any single candidate.
Building an AI platform team — training infra, serving infrastructure, feature stores, MLOps, evaluation frameworks — that can support multiple product lines without becoming a bottleneck.
Responsible AI leadership — Heads of Model Risk, AI Governance, and AI Ethics who can operate under DPDP, GDPR, and emerging EU AI Act frameworks while not blocking commercial velocity.
Chief Data Officer hires — CDOs who can unify data contracts, governance, and a single logical data plane while enabling self-serve analytics and AI-ready data products.
Senior individual contributor hiring — principal research scientists, distinguished AI engineers, and applied research leads where a single hire can shift technical trajectory.
GCC AI leadership — site leads for global AI platform teams who can operate as dual-reporting leaders to global AI heads and Indian country MDs.
What We Look For in AI & Data Science Leaders
Across mandates, ai & data science leadership tends to cluster into a small set of archetypes. We calibrate each search against the profile your board actually needs — not the one most commonly available.
The Chief AI Officer
Leader who combines research credibility (typically a PhD from a top-tier institution and published work or production LLM deployments) with applied delivery experience. Fluent in board-level AI governance and commercially bilingual with product and engineering organisations.
The Applied AI VP
Operator who has shipped production fine-tuned, RAG-based, or agentic AI systems at scale. Deep understanding of evaluation, guardrails, prompt engineering at system level, and the MLOps architecture that makes applied AI reliable.
The ML Platform Leader
Infrastructure engineer who has built training and serving infra for foundation-model or high-volume applied-AI workloads. Fluent in GPU scheduling, distributed training, inference optimisation, and the feature-store / model-registry stack.
The Chief Data Officer
Data leader who has rebuilt an enterprise data platform for AI-ready consumption — data contracts, governance, single logical data plane, and self-serve analytics. Often has operated inside BFSI, consumer, or healthcare contexts where data governance is a board-reported topic.
The Principal Research Scientist
Individual contributor with published work in top-tier venues (NeurIPS, ICML, ICLR) or demonstrable contributions to widely-used open-source foundation models. Often a PhD from a top-tier ML research group, with subsequent production experience at a foundation-model lab.
The AI Governance Leader
Risk, compliance, or legal leader who has built a model-risk or AI-governance function. Operates under DPDP, GDPR, and EU AI Act frameworks; understands the intersection of model validation, disclosure, and board-committee governance.
Regulatory & Compensation Context
Regulatory Backdrop
AI leadership is hired into an intensifying regulatory envelope. India's DPDP Act has introduced data fiduciary obligations, consent architecture, and cross-border transfer restrictions that shape both ML training data practices and inference pipelines. The RBI's Model Risk Management Guidelines (MRMG) require model inventory, independent validation, and ongoing performance monitoring for any AI used in credit, fraud, or consumer-facing financial decisions. SEBI and IRDAI have both issued consultation papers on AI/ML in capital markets and insurance — not yet fully binding but shaping governance expectations. Globally, the EU AI Act's risk-tier classification, GDPR's Article 22 on automated decision-making, and US state-level AI regulation (New York City Local Law 144, Colorado's AI Act) materially affect leadership candidates hired into India-based roles that serve global products. Responsible AI committees, model-risk registries, and independent AI audits are now standing board-committee topics at leading enterprises. Candidates for senior AI roles are increasingly evaluated on their ability to operate under these frameworks, not just their technical depth.
Compensation Architecture
AI leadership compensation sits at the top end of the technology market. A Chief AI Officer at a well-capitalised AI-first SaaS franchise or a large enterprise commands ₹6-15 crore fixed cash with 1-3% equity and meaningful bonus opportunity tied to technical and product milestones. VPs of Applied AI and ML Engineering price at ₹4-8 crore fixed with 0.5-1.5% equity. Principal research scientists and distinguished engineers — the IC roles — have the most stretched compensation market: US-benchmarked USD cash packages of $400K-$800K plus equity, dual-jurisdiction structures, and signing bonuses that can match fixed cash. CDOs price at ₹3-6 crore fixed. AI Governance and Model Risk leaders, particularly in BFSI, sit at ₹3-5 crore fixed with strong cash bonuses. For GCC AI site leads, comp tracks the global parent's AI leadership band (typically USD-anchored) rather than Indian country-MD bands. Retention is a first-class problem — counteroffers from foundation-model labs and global hyperscalers are now standard on every senior AI exit; we advise clients on retention architecture (refreshers, secondaries, confidential scope expansions) alongside the initial hire.
Roles We Typically Place
Why Gladwin International Leadership Advisors for AI & Data Science
Chief AI Officer and Head of AI searches across AI-first startups, enterprise AI platforms, and GCC AI centres.
Applied AI leadership — VPs of Applied AI, Heads of AI Products, and GMs for AI-native product lines.
ML platform and infrastructure leadership — VPs of ML Engineering, Heads of MLOps, and ML Infra leads for foundation-model and applied-AI companies.
Chief Data Officer and data governance leadership for enterprises rebuilding their data architecture for AI.
Senior individual contributor placements — principal research scientists, distinguished engineers, and AI architects.
Responsible AI, AI governance, and model-risk leadership for BFSI, healthcare, and regulated-industry clients.
Organisations We Serve
Foundation-model research labs and AI-first product companies
Applied AI SaaS companies (legal AI, customer-support AI, healthcare AI)
Enterprise AI platform companies
GCCs housing global AI platform teams
BFSI, healthcare, and regulated-industry enterprises building internal AI capability
Consumer internet and SaaS companies standing up first AI leadership roles
AI & Data Science leaders assessed on the Technology “NEXUS” framework
Seven dimensions calibrated for technology and digital leadership excellence. Dimensions are calibrated for ai & data science mandates where relevant.
Parent Practice
Return to Technology & Digital
Discuss a AI & Data Science Mandate
Confidential · No obligation
Response within 4 business hours · All enquiries handled by a senior practice partner · Strictly confidential