Applied AI ML Lead - Agent Builder Platform

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in Jersey City seeks an Applied AI and Machine Learning Lead to architect and deliver agentic AI products across enterprise platforms. You will translate complex business needs into production-grade solutions and raise delivery standards with measurable impact.

You will lead design and delivery of end-to-end AI/LLM use cases, own core services, and partner with product managers to shape roadmaps, governance, and reliability across engineering, data, and security teams.

Qualifications

• Formal training or certification in applied AI/ML with 5+ years of practical experience.
• Strong Python development skills and software engineering fundamentals.
• Experience deploying ML/LLM-powered systems to production.
• Knowledge of prompt engineering and retrieval-augmented generation.
• Ability to design reliable, scalable services with incident readiness.
• Proven ability to lead technical decisions and deliver impact.

Responsibilities

• Lead end-to-end delivery of agentic AI and large language model-powered use cases from problem framing and 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, 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.

Skills

Python programming

ML deployment

Technical leadership

Prompt engineering

Communication skills

System design

Tools

Docker

Kubernetes

LangGraph

LlamaIndex

Google ADK

Vector databases

Embedding pipelines

Job description

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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