Tsai Center for Innovative Thinking at Yale (Tsai CITY)

AI Engineer

• Design, build, test and operate AI services and workflows from proof of concept through production deployment.
• Develop capabilities across classification, extraction, summarisation, entity resolution, stakeholder mapping, retrieval, risk detection and insight generation.
• Select the appropriate combination of deterministic software, machine learning, retrieval, LLMs, agents and human review for each use case.
• Build clear APIs and integrations that connect AI capabilities to Genie and related internal systems.
• Own the quality, reliability and maintainability of the components you ship, including production support and continuous improvement.
• Build and improve pipelines for ingestion, cleaning, enrichment, labelling, chunking, indexing and retrieval.
• Protect sensitive and customer-specific data through secure handling, access controls, workspace boundaries and careful use of external model providers.
• Identify and mitigate risks including prompt injection, data leakage, stale context, unsupported claims, excessive autonomy and unsafe tool permissions.
• Translate ambiguous customer and business workflows into clear technical requirements, experiments and acceptance criteria.
• Communicate technical trade-offs, limitations and evidence clearly to both technical and non-technical colleagues.
• Maintain clear technical documentation, implementation notes and decision records in shared systems such as Jira and Confluence.
• Participate actively in code reviews, design reviews, sprint planning, incident learning and architectural discussions.

Artificial Intelligence

• 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.

Head of AI

Manager

• Support the delivery of intelligence outcomes for a portfolio of enterprise clients.
• Work with senior Engagement Managers and product teams to translate client goals into clear deliverables.
• Act as the verification and judgment layer on AI-assisted outputs.
• Prepare executive briefings and syntheses.
• Build and maintain client relationships across external affairs, risk, and strategy teams.

AI Engineer

Head of AI

Engagement Manager, Mid