Vista previa de la oferta
Applied AI ML Lead - Agent Builder Platform
lead · Technology / Software Development
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.
#LI-RB3