Applied AIML Lead – Agentic AI & Python

Overview

In this role you drive end-to-end AI/ML delivery for IPB, turning business problems into scalable, production-ready AI solutions. You will partner with cross-functional teams to shape, build, and deploy agentic AI and guardrails that impact advisors and clients across international markets. You’ll set quality standards, advance Responsible AI, and contribute to governance and education efforts. This is a hands-on, impact-focused opportunity to scale trusted AI in private banking.

Responsibilities

• Own end-to-end delivery of priority IPB AI/ML use cases from framing to deployed production services with measurable impact
• Lead engineering development of agentic AI and LLM-powered products for IPB advisors and clients across markets
• Set engineering quality standards through code reviews, design, and mentoring
• Establish and operate Responsible AI controls, guardrails, evaluation frameworks, observability, and model risk controls
• Partner with IPB business stakeholders to surface AI/ML opportunities and fund them
• Represent AIML in governance forums, ensuring regulatory and data-privacy considerations are reflected in designs
• Contribute to GenAI education through training content and mentoring
• Champion diversity, inclusion, and respect within the team

Key requirements

• Formal AI/ML training or certification with applied experience
• Advanced proficiency in Python and modern software engineering practices
• Fluent with AI coding tools (e.g., Claude Code, GitHub Copilot)
• Hands-on experience building, evaluating, and deploying ML models into production
• Experience with Large Language Models, prompt engineering, RAG, fine-tuning, and agentic frameworks
• Experience with CI/CD, containerisation, and cloud-native deployment
• Proven ability delivering system design, development, testing, and operational stability for ML/data-heavy systems
• Strong communication with senior stakeholders and ability to translate technical concepts for non-technical audiences
• Experience applying new methods to complex tech problems across disciplines
• Master’s degree in Computer Science, Data Science, Engineering, or related quantitative field

• strong communication
• stakeholder engagement
• mentoring and coaching
• Python
• CI/CD
• containerisation

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