Join a team that owns end-to-end delivery of priority IPB AI/ML use cases, from problem framing and business case through to deployed, monitored production services with measurable advisor and client impact.
As an Applied AI ML Lead, within the Corporate Technology Team, you will lead the engineering build of agentic AI and LLM-powered products serving IPB advisors and clients across international markets.
Job Responsibilities:
• Owns end-to-end delivery of priority IPB AI/ML use cases, from problem framing and business case through to deployed, monitored production services with measurable advisor and client impact
• Leads the engineering build of agentic AI and LLM-powered products serving IPB advisors and clients across international markets
• Sets the engineering quality bar for the team's AI products through code reviews, technical design, and pairing with peers and junior engineers
• Establishes and operates Responsible AI controls in production (guardrails, evaluation frameworks, observability, and model risk controls) to firm-wide standards
• Acts as a primary technical partner to IPB business stakeholders, surfacing new AI/ML opportunities and shaping them into funded workstreams
• Represents the AIML team in firm-wide AI/ML governance and engineering forums; ensures cross-border, regulatory, and data-privacy considerations are reflected in solution design.
Required qualifications, capabilities, and skills:
- Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field (or equivalent applied experience); Formal training or certification on AI/ML engineering concepts and 5+ years applied experience
- Advanced proficiency in Python and modern software engineering practices (testing, design patterns, code review, version control)
- Fluent with AI coding tools (e.g., Claude Code, GitHub Copilot) as a core part of day-to-day software development, with the judgement to know when to lean on them and when not to
- Hands-on experience building, evaluating, and deploying machine learning models into production and practical experience with Large Language Models, including prompt engineering, RAG, fine-tuning, and agentic frameworks
- Practical experience with CI/CD, containerisation, and cloud-native deployment patterns
- Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data-intensive systems
- Experience applying new methods to determine solutions for complex technology problems across multiple technical discipline.
Preferred qualifications, capabilities, and skills:
- Industry-recognised cloud / GenAI certification (e.g., AWS Certified Generative AI Developer - Professional, or similar)
- Experience within financial services technology, particularly wealth, private banking, or asset management
- Experience with Databricks, Kubernetes, or comparable ML / cloud platforms
- Experience designing or contributing to AI governance, model validation, or guardrail frameworks