Applied AI ML Lead- Agentic AI & Python

Overview

In this VP role, you drive end-to-end AI/ML solutions for IPB, translating business problems into production-grade AI services. You will lead cross-functional teams to build agentic AI and LLM-powered products with strong governance, guardrails, and measurable impact for advisors and clients. You’ll partner with senior stakeholders, shape funded AI/ML initiatives, and mentor engineers while advancing responsible AI practices. This is a high-impact opportunity to influence AI strategy and delivery in a global private banking context.

Responsibilities

• Own end-to-end delivery of priority IPB AI/ML use cases from problem framing to production deployment
• Lead engineering build of agentic AI and LLM-powered products for IPB advisors and clients globally
• Set engineering quality bar via code reviews, design decisions, and pairing with peers/junior engineers
• Establish Responsible AI controls in production (guardrails, evaluation, observability, model risk) to firm standards
• Surface AI/ML opportunities to IPB business stakeholders and shape them into funded workstreams
• Represent AIML in firm-wide governance and ensure regulatory and data-privacy considerations are reflected in designs
• Contribute to GenAI education through training content and mentoring
• Champion diversity, inclusion, and respect across the team

Key requirements

• Formal training or certification in software engineering concepts
• 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, agentic frameworks)
• Experience delivering system design, development, testing, and operational stability for ML/data-intensive systems
• Strong communication skills to engage senior stakeholders and translate technical concepts
• Experience applying new methods to solve complex technology problems across multiple disciplines
• MSc in Computer Science, Data Science, Engineering, or related field

• strong communication
• stakeholder engagement
• mentoring and coaching
• Python
• testing, design patterns, version control
• AI coding tools: Claude Code, GitHub Copilot

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