Head of AI

Tsai Center for Innovative Thinking at Yale (Tsai CITY)

Head of AI overseeing technical strategy and delivery of AI-native intelligence products for enterprise teams. Collaborating across disciplines to enhance customer decision workflows with reliable data.

Posted 7/5/2026full-timeSingapore • 🇸🇬 SingaporeLeadWebsite

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud Platform

About the role

Key responsibilities & impact

Own the technical strategy, systems, team and operating model required to turn TSC's data into reliable, differentiated and commercially valuable AI-powered intelligence.
Define TSC's AI technical strategy across stakeholder mapping, classification, summarisation, entity resolution, risk detection, insight generation and workflow automation.
Identify and frame high-value AI opportunities based on customer problems, decision workflows and TSC's differentiated data assets.
Partner with Product to translate ambiguous customer needs into measurable product requirements, acceptance criteria and release sequencing.
Define the appropriate technical approach for each use case, including deterministic software, machine learning, retrieval, LLMs, agents and human review.
Own the end-to-end lifecycle of material AI capabilities, from data and evaluation design through deployment, monitoring, incident response and continuous improvement.
Build TSC's evaluation system for AI products, including golden datasets, regression tests, retrieval-quality checks, extraction and hallucination metrics, human-review thresholds and business KPIs.
Define release-quality standards and ensure AI features are observable, testable, traceable and reversible.
Establish governance for model and agent use, including prompt injection, data leakage, tool permissions, approval workflows, audit logs and customer-specific restrictions.
Own the data foundations behind TSC's intelligence products: ingestion, transformation, enrichment, entity resolution, labelling, retrieval, freshness, telemetry and feedback loops.
Architect production AI systems using LLM APIs, specialised and open-source models, retrieval, deterministic orchestration and tool-using agents where appropriate.

Requirements

What you’ll need

Typically 8+ years building production software, data or AI systems, including substantial experience leading multidisciplinary technical teams.
Exceptional evidence of production delivery is more important than a specific tenure threshold.
A proven record of shipping and operating production AI or data systems, not only prototypes, demonstrations or research projects.
Practical depth in LLM systems, retrieval-augmented generation, orchestration, embeddings, classification, summarisation, extraction and evaluation.
Strong experience with data pipelines and cloud data platforms.
Experience with GCP, BigQuery, AlloyDB, Vertex AI or comparable platforms is preferred.
Experience designing systems for enterprise or sensitive-data environments, including tenant isolation, access controls, auditability, vendor risk and secure model usage.
The ability to review code, data models, system architecture and evaluation results in depth, and to contribute directly to technical problem-solving when required.
The ability to explain AI strategy, architecture, quality, cost, risk and roadmap trade-offs to executives, customers and non-technical stakeholders.

Benefits

Comp & perks

Professional development opportunities
Flexible working arrangements

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Machine LearningEntity ResolutionClassificationSummarisationRisk DetectionWorkflow AutomationEvaluation DesignData TransformationExtractionOrchestration

Soft Skills

Technical Problem-SolvingStakeholder CommunicationTeam Leadership