Applied AI ML Lead- Agentic AI & Python
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Key skills for this role
Key Skills for This Role
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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
About JPMorgan Chase
Global financial services and investment banking firm.
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