Manager, AI Engineering

About the role

The Data Science team builds analytics, AI, automation and technology solutions that improve client service delivery and strengthen professional services offerings. As Manager, AI Engineering, you will lead a small team of AI engineers while staying substantially hands-on, and be accountable for the quality, reliability and responsible use of the AI solutions your team delivers.

Key responsibilities

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

Required profile

• 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 leading engineers or serving as a technical lead.
• 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.

Required skills

• Python, SQL and machine learning libraries such as PyTorch and scikit-learn.
• LLM and agent frameworks such as LangChain/LangGraph.
• LLM evaluation, vector search and cloud AI services such as Azure OpenAI and Azure AI Search.
• Hands-on experience building data pipelines is an advantage.