**Senior Lead Software Engineer - Agent Builder Platform**
Spearhead team-building and delivery of agentic AI and large language model use cases, from inception to deployment. Key responsibilities include leading core platform services, setting high engineering quality standards, and driving cross-functional collaboration. Apply your 5+ years of AI/ML experience and advanced Python proficiency to translate complex business needs into reliable, production-ready solutions at JPMorgan.
Full Job Description
Location: Jersey City, NJ, United States
Help shape how teams across the firm build and deploy agentic artificial intelligence productsat 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 Senior Lead Software Engineer - 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 experienceAdvanced 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. Lead an Agent Builder platform enabling secure, scalable agentic AI products for engineering teams across the organization. 
