We're looking for a hands-on AI engineer ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies.
As an Applied AI ML Lead - Vice President at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market-leading AI products in a secure, stable, and scalable way. You will translate business problems into agentic AI and machine learning solutions, taking models from concept through to production-grade services with measurable client and advisor impact.
You will be responsible to the Head of AIML in IPB Tech for the end-to-end design, build, and production delivery of priority IPB AI/ML use cases, with particular focus on agentic AI applications, generative AI guardrails, and production ML supporting advisor and client journeys.
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