Applied AI ML Lead- Agentic AI & Python

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 the globe.
• 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
• Contributes to the team's GenAI education programme through training content, knowledge-sharing sessions, and mentoring of junior engineers and interns
• Champions the firm's culture of diversity, Opportunity, inclusion, and respect

Required qualifications, capabilities, and skills

• Formal training or certification in software engineering concepts and expert 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
• Practical experience with Large Language Models, including prompt engineering, RAG, fine-tuning, agentic frameworks, skills.
• Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data-intensive systems
• Strong communication skills with confidence engaging senior business stakeholders and translating technical concepts for non-technical audiences
• Experience applying new methods to determine solutions for complex technology problems across multiple technical disciplines
• MSc in Computer Science, Data Science, Engineering, or a related quantitative field

Preferred qualifications, capabilities, and skills

• Postgraduate-level qualification in data science, artificial intelligence, or machine learning
• Practical experience with CI/CD, containerization, and cloud-native deployment patterns
• 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