Head of AI

Responsibilities

• 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.

Qualifications

• 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.