Assistant General Manager, AI Engineering

Role Summary

We are looking for an Assistant General Manager, AI Engineering to lead the forward-deployed AI engineering function, partnering directly with business teams to identify, build and scale production-grade AI solutions that deliver measurable business outcomes.

Reporting to the Head of Data & AI, the role is accountable for end-to-end delivery, engineering standards, technical architecture, production reliability, and the development of a high-performing AI engineering team.

Job Responsibilities:Forward deployed delivery

• Partner with business stakeholders to understand workflows, test assumptions and define measurable outcomes.
• Lead solutions from discovery and prototype to production, user adoption and support; turn reusable components into shared capabilities.

AI applications and integration

• Lead the development of services, APIs and user-facing workflows using LLMs, retrieval and tool execution.
• Integrate enterprise applications through authenticated APIs and MCP where appropriate, selecting deterministic workflows, agents or conventional ML based on the problem.

Context engineering

• Design retrieval, chunking, embeddings, reranking and context assembly.
• Manage instructions, tool definitions, conversation state, memory, freshness and token budgets; preserve source provenance and enforce access permissions.

Agent harness engineering

• Build the runtime around models: tool contracts, orchestration, state persistence, checkpoints, bounded execution, retries, timeouts and recovery.
• Prevent duplicate side effects and enforce approval boundaries for consequential actions.

Evaluation and production operations

• Establish representative evaluation datasets and regression gates for model, prompt, retrieval and tool changes.
• Trace failures and monitor task success, groundedness, latency and cost per completed task.
• Lead release readiness, rollback and incident diagnosis with platform teams.

Data and model foundations

• Build reliable ingestion and transformation pipelines with Data Engineering.
• Maintain data quality, schemas and lineage.
• Apply deep learning or conventional ML where appropriate, with robust validation and monitoring.

Technical leadership and controls

• Set architecture and code quality standards; prioritise delivery with business owners.
• Work with Cyber Security to implement identity, least privilege, secrets management, auditability and prompt-injection defences.
• Ensure coding agents are used with appropriate testing, review and accountable release decisions.

People management

• Lead and develop a team of AI engineers through clear goals, work allocation, regular feedback and performance reviews.