Applied AI ML Lead - Agent Builder Platformnew

Help shape how teams across the firm build and deploy agentic artificial intelligence products—at scale, with quality, and with measurable impact. You will work on a high-visibility platform that accelerates engineering teams, raises delivery standards, and turns complex business needs into reliable production outcomes. Join a team where strong engineering, thoughtful collaboration, and continuous learning are core to how we operate.

As a Applied AI and Machine Learning Lead  - Agent Builder Platform at JPMorganChase within Enterprise Technology, AI and Machine Learning & Data Platforms, you will lead the technical design and delivery of agentic AI products and platform capabilities used by engineering teams across the organization. You will translate high-impact business problems into production-grade solutions, from discovery and design through deployment and ongoing operations. You will set a high engineering quality bar while partnering closely with product and stakeholder groups to deliver measurable outcomes.

Job responsibilities

• Lead end-to-end delivery of agentic AI and large language model-powered use cases from problem framing and technical design through production deployment and monitoring.
• Own core platform services and reusable components that enable teams to build, evaluate, and operate AI agents safely at scale.
• Establish engineering standards through hands-on system design, rigorous code review, and mentorship to improve reliability, maintainability, and developer experience.
• Build and operationalize evaluation, testing, and observability capabilities (tracing, metrics, logs, and analytics) to continuously improve solution quality.
• Implement robust safety and governance patterns, including guardrails, access controls, and audit-ready operational practices aligned to enterprise expectations.
• Partner with product managers and stakeholders to shape roadmaps, define success metrics, and prioritize work that delivers measurable business impact.
• Drive cross-functional alignment across engineering, data, security, and risk partners to ensure solutions are secure, stable, and scalable.
• Contribute to technical documentation, reference implementations, and enablement content that accelerates adoption and responsible usage.

Required qualifications, capabilities and skills

• Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience
• Advanced proficiency in Python with strong software engineering fundamentals, including testing, design patterns, version control, and code review practices.
• Hands-on experience building, evaluating, and deploying machine learning or large language model-enabled systems into production environments.
• Practical experience with prompt engineering and retrieval-augmented generation, including evaluation methods and quality measurement.
• Experience designing and operating reliable services, including incident response readiness, performance tuning, and operational stability for data-intensive systems.
• Demonstrated ability to lead technical decisions and deliver outcomes through ambiguity, balancing speed, risk, and long-term maintainability.
• Strong communication skills with the ability to explain technical trade-offs to both technical and non-technical stakeholders.

Preferred qualifications, capabilities and skills

• Experience with agent orchestration frameworks (for example, LangGraph, LlamaIndex, Google ADK or custom orchestration) and evaluation tooling for large language model systems.
• Experience with continuous integration and continuous delivery practices and containerization (Docker and Kubernetes) for production deployments.
• Familiarity with vector databases, embedding pipelines, or graph-based memory approaches used in retrieval-augmented generation solutions.
• Experience with cloud and machine learning platforms (for example, Amazon Web Services, Databricks, or comparable platforms).
• Experience contributing to AI governance, validation approaches, or guardrail frameworks in enterprise settings.

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