Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let’s push the boundaries of what's possible together
As a Applied AI ML Director at JPMorgan Chase within the Trust Estate Technology Team under Asset & Wealth Management line of business, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job Responsibilities:
- Defines and owns reference architectures for agentic AI, including LLM orchestration, tool use, retrieval, guardrails, evaluation harnesses, and observability. Lead hands-on build in Python with PyTorch or TensorFlow where needed.
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Establish and promote a library of reusable GenAI/ML engineering assets, including reference implementations, standardized templates/SDKs, shared RAG components (ingestion, chunking, embedding, indexing, retrieval), and deployment patterns.
- Establishes reusable components (prompt management, evaluators, safety filters, memory stores, connectors, RAG pipelines) to accelerate delivery across AWM and partner lines of business.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Build and operationalize agentic GenAI workflows (planning/execution patterns, tool calling, state management, retries) with appropriate guardrails, permissions, and observability.
- Liaise with firmwide AI/ML stakeholders to drive standards, interoperability, adoption, and reuse of shared frameworks.
Required qualifications, capabilities, and skills:
- Formal training or certification in Artificial Intelligence & Machine Learning concepts and 10+ years applied experience.
- Practical experience delivering system design, application development, testing, and operational stability
- Hands-on experience building agentic AI solutions and LLM orchestration (prompt engineering, tool use, retrieval, evaluators, guardrails).
- Strong Python engineering skills; experience with PyTorch or TensorFlow.
- Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data
- Demonstrated expertise in Artificial Intelligence and Machine Learning technologies, including proficiency with frameworks such as Langchain, LangGraph, Hugging Face and RAG
- Ability to advise cross-functional teams on technological matters within the domain of Agentic AI Platforms and building agentic customer experiences
- Contributes to the development of technical methods in Agentic Customer Experiences in line with the latest product development methodologies
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Demonstrated experience in system design, including architecting scalable and reliable solutions, selecting appropriate technologies, defining system components and their interactions, and ensuring alignment with business requirements and performance goals through detailed documentation and collaborative design reviews.
- Cloud deployment experience (AWS or Azure) for AI/ML workloads; reliability, scalability, and cost optimization.
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
Preferred Qualifications, Capabilities, and Skills:
- Hands-on practical experience in building agentic AI platforms for building AI-driven customer experiences
- Advanced knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines -- cloud, artificial intelligence, machine learning, AI agents
- Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines
- Ability to present and effectively communicate with Senior Leaders and Executives
- Practical cloud native experience