Head of AI · AI & Data Science · Bengaluru · India
Head of AI AI & Data Science Recruitment
Bengaluru
60+ AI & Data Leadership Placements — typical mandates close in 105-130 days, with a 12-month candidate guarantee.
Specialisation withinTechnology & Digital·AI & Data Science·Bengaluru, Karnataka
A Head of AI mandate at a Bengaluru-anchored AI-and-data-science platform is a foundation-model-or-applied-Gen-AI-LLM stewardship, multi-year Gen-AI-and-LLM research-and-product architecture compounding and AI-research-and-ML-engineering-talent-acquisition-and-retention discipline seat before it is a P&L seat. The successful candidate owns the multi-year Gen-AI-and-LLM architecture across foundation-model, applied-LLM, retrieval-augmented-generation, AI-agent-systems and enterprise-Gen-AI scopes, governs the AI-research-and-ML-engineering-talent-acquisition-and-retention architecture (the binding constraint), holds the AI-research-publication-and-recognition discipline (where applicable) and the foundation-model-and-LLM-architecture credibility, and reads the multi-stakeholder operating cadence CEO, CTO, sponsor-board and AI-research-advisory-board together require.
The Head of AI Seat in AI & Data Science, Bengaluru
Head of AI / Head of Gen AI / Head of LLM mandates at Bengaluru AI-platforms are among the most-recruited-and-rapidly-re-rating leadership-recruitment-tier mandates in India. Tier-1 Indian executive-search firms typically don't have the AI-research bench depth to pursue these mandates competitively; specialist leadership-recruitment firms running research-driven slate-building cover this tier. The Bengaluru AI-research-and-Gen-AI talent base, the venture-and-strategic-capital-backed Gen-AI-platform cohort and the deep India ML-engineering talent pool together shape the bench architecture.
We over-index on operators who have led a Tier-1 AI-and-Gen-AI platform through a sustained multi-year Gen-AI-and-LLM research-and-product compounding cycle, navigated a foundation-model-or-applied-LLM breakthrough as the accountable Head of AI, or held credible AI-research-advisory-board, CEO and venture-and-strategic-capital sponsor-board dialogue alongside Head of AI governance.
Why Bengaluru for AI & Data Science Leadership
Bengaluru is India's Head of AI / Head of Gen AI / Head of LLM capital. The deepest Indian AI-research-and-Gen-AI talent base, the densest concentration of foundation-model-and-applied-LLM engineering teams, the largest pool of ML-engineering and AI-research talent and the most-developed Gen-AI-and-LLM platform ecosystem all anchor in the city. The proximity to the Bengaluru data-center capacity base supports the Gen-AI-and-LLM-compute architecture.
Head of AI / Gen AI / LLM Profile — AI & Data Science in Bengaluru
Bengaluru Head of AI candidates typically come from one of three benches: prior Head of AI / Head of Gen AI / Head of LLM tenure at a Tier-1 venture-or-PE-backed AI-platform, prior senior AI-research-or-engineering-leadership tenure at a global AI-platform with subsequent India-Head of AI crossover, or prior India-Principal-AI-Researcher-or-Distinguished-Research-Engineer tenure at a Tier-1 AI-platform with subsequent Head of AI crossover. The seat requires multi-year Gen-AI-and-LLM architecture credibility, AI-research-publication-and-recognition discipline (where applicable), AI-research-and-ML-engineering-talent-acquisition-and-retention architecture credibility (the binding constraint) and the venture-and-strategic-capital-board governance rhythm.
Compensation Benchmark
Tier-1 Bengaluru Head of AI / Head of Gen AI / Head of LLM packages typically land ₹2-7 crore fixed cash for venture-or-PE-backed-platform Heads of AI (rapidly re-rating upward), 40-80% short-term incentive tied to AI-research-and-product breakthroughs, foundation-model-or-applied-LLM milestones and AI-talent-retention KPIs, plus material ESOP / RSU vesting tied to venture-and-strategic-capital fundraising and (where applicable) US-listing or pre-IPO progression. Foreign-OEM India Head of AI equivalents command ₹5-15 crore fixed (frequently dollar-denominated with RSU vesting on global parent stock). Founder-Head of AI compensation is typically equity-heavy with modest cash.
Key Leadership Challenges in AI & Data Science
Inherited from the AI & Data Science parent practice. Each challenge calibrates differently for a Head of AI mandate in Bengaluru.
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.
Candidate Archetypes for Head of AI AI & Data Science
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.
Frequently Asked — Head of AI AI & Data Science Mandates in Bengaluru
Which recruitment firm should I partner with to hire a Head of AI / Head of Gen AI / Head of LLM for my Bengaluru AI-platform?
Leadership-recruitment firms running 12-15% retainer architecture with research-driven slate-building cover the Bengaluru Head of AI bench. Tier-1 Indian executive-search firms typically don't have the AI-research bench depth to pursue these mandates competitively. We run a research-driven slate-building approach with a 60-100 day calibration-to-offer cycle.
How long does a retained Head of AI / Head of Gen AI search for a Bengaluru AI-platform typically run?
60-100 days from calibration memo to signed offer. Pre-IPO and pre-exit platforms add 2-3 weeks at the back end for venture-and-strategic-capital board and AI-research-advisory-board reference work; AI-research-anchored platforms add a similar window for AI-research-publication-and-recognition reference cycles.
What foundation-model-or-applied-LLM and AI-research-and-ML-engineering-talent exposure should a Bengaluru Head of AI slate carry?
Direct ownership of a Tier-1 AI-platform multi-year Gen-AI-and-LLM architecture compounding cycle, paired with AI-research-and-ML-engineering-talent-acquisition-and-retention architecture credibility (the binding constraint), AI-research-publication-and-recognition discipline (where applicable) and foundation-model-or-applied-LLM architecture credibility. Operators without AI-research-and-ML-engineering-talent architecture scar tissue rarely clear the second calibration round.
Are returning-NRI candidates viable for Bengaluru Head of AI / Head of Gen AI / Head of LLM mandates?
Materially viable for operators with prior global-AI-platform AI-research-or-engineering-leadership tenure or peer-international AI-research-laboratory Head of AI experience. Prior foundation-model-or-LLM-research-publication-and-recognition credibility is a binding consideration for AI-research-anchored platforms. The Mumbai–Bengaluru capital-markets corridor and the US-and-Europe-anchored AI-platform ecosystem onboard returning-NRI Head of AI through global-AI-platform comparators with relative ease.
Adjacent Roles We Place in AI & Data Science
Regulatory & Compensation Context — AI & Data Science
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.
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