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.
Coach technical and career development, address delivery blockers and foster strong team ownership.

Job Requirements

Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Data Science, Artificial Intelligence, or a related discipline.
10+ years of technology/software engineering experience, including 5+ years of hands-on experience delivering AI/ML solutions, including production deployment of LLM-based applications or agents.
Strong expertise in LLM engineering, including RAG, embeddings, structured outputs, tool calling and context engineering.
Strong proficiency in Python and SQL, with experience in APIs, integrations, automated testing, Git, CI/CD, containers, authentication and production debugging.
Experience with AI evaluation, failure analysis, reliability monitoring, data pipelines, databases, data quality and access-controlled retrieval.
Experience with cloud deployment, relational and analytical data stores, model APIs, and delivery pipelines, with demonstrated ability to make sound architecture, technology, and operational decisions. Familiarity with equivalent technologies and frameworks is welcome.
Strong understanding of machine learning and deep learning fundamentals, including transformers, embeddings, model validation and selecting between prompting, retrieval, fine-tuning and conventional ML.
Proven ability to translate business problems into measurable outcomes, lead technical architecture and delivery, establish engineering standards, and manage delivery across business, technology and security teams.
Experience working with business users and product owners, and collaborating with data engineering, infrastructure, DevOps, security, architecture, and application teams to deliver and operate integrated solutions.
Proven track record in leading and developing AI engineering teams, including setting strategic goals, managing resources, developing talent, coaching engineers, and driving performance.
Experience with PyTorch, open-weight models, MLOps, multimodal/document AI, MCP or major AI platforms such as Azure OpenAI, OpenAI, Anthropic, AWS or Google AI is advantageous.