About This Role
This senior AI engineering role at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team involves providing deep engineering expertise across agile teams. You will enhance, build, and deliver trusted market-leading AI products in a secure, stable, and scalable way, translating business problems into agentic AI and machine learning solutions from concept to production with measurable client and advisor impact.
Responsibilities
- Own 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
- Lead the engineering build of agentic AI and LLM-powered products serving IPB advisors and clients across international markets
- Set the engineering quality bar for the team's AI products through code reviews, technical design, and pairing with peers and junior engineers
- Establish and operate Responsible AI controls in production (guardrails, evaluation frameworks, observability, and model risk controls) to firm-wide standards
- Act as a primary technical partner to IPB business stakeholders, surfacing new AI/ML opportunities and shaping them into funded workstreams
- Represent the AIML team in firm-wide AI/ML governance and engineering forums; ensure cross-border, regulatory, and data-privacy considerations are reflected in solution design
- Contribute to the team's GenAI education programme through training content, knowledge-sharing sessions, and mentoring of junior engineers and interns
Requirements
- 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
- 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
- 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
- Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field (or equivalent applied experience)
