Manager, AI Engineering

Our Data Science team develops analytics, artificial intelligence, automation and technology solutions that improve client service delivery and strengthen our professional services offerings.

Your responsibilities

As Manager, AI Engineering, you will lead a small team of AI engineers, remaining substantially hands-on, and be accountable for the quality, reliability and responsible use of the AI solutions the team delivers.

Team leadership: Lead, coach and develop AI engineers, and set priorities and allocate work. Establish standards for solution design, testing, documentation and code review, and promote responsible use of AI-assisted development tools.
AI solution delivery: Translate business needs into practical AI solutions, and lead the design, development and deployment of LLM-based agentic workflows, retrieval-augmented generation (RAG) pipelines and machine learning models for audit and firm processes. Manage scope, timelines and trade-offs, and deliver solutions through well-defined APIs that follow the platform's CI/CD standards, for integration into the firm's central platform.
Evaluation and governance: Define how AI outputs are evaluated and reviewed before they are relied upon, including human-in-the-loop controls. Work with IT and governance team on data security, client-data confidentiality and responsible AI.
Data pipelines: Build and maintain the data pipelines that AI solutions depend on, keeping outputs traceable to their sources.
Stakeholder collaboration and support: Work with business users and project sponsors to agree priorities, communicate AI capabilities and limitations, and support adoption. Work with fellow Data Science managers to staff and deliver cross-team projects. Ensure solutions are documented, monitored and supportable after go-live, and agree support handovers with Full-Stack Engineering for AI features within applications.
Research: Track developments in machine learning, LLMs and agentic frameworks, and bring the most promising into practical use.

Requirements

Degree in Computer Science, Data Science, Statistics, Mathematics or a related field.
At least 5 years of experience building and deploying machine learning solutions in production, with a strong foundation in machine learning and model evaluation.
At least 2 years of hands-on experience developing and deploying LLM-based agentic workflows in production.
Experience in leading engineers or serving as a technical lead.
Strong working knowledge of Python, SQL, machine learning libraries (e.g. PyTorch, scikit-learn) and LLM and agent frameworks (e.g. LangChain/LangGraph), with sufficient hands-on depth to review designs and contribute when needed.
Experience with LLM evaluation, vector search and cloud AI services (e.g. Azure OpenAI, Azure AI Search); experience building data pipelines is an advantage.
Strong communication and stakeholder management skills, with sound judgement on AI risk and governance.
Experience in professional services, audit or another regulated environment is an advantage.