Bulge Bracket Investment Banks

Posted 6 days ago

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**- Applied AI ML Lead** - Define and drive AI/ML agent platform roadmap, focusing on outcomes, reliability, and usability. - Lead end-to-end delivery of core agent platform components, including SDKs, reference implementations, and integration patterns. - Collaborate with cross-functional teams to align requirements, balancing speed, risk, and long-term maintainability. - Set technical direction, raise engineering standards, and mentor team members. - 5+ years in applied AI/ML, proven leadership, strong programming skills, and cloud expertise. - Experience with agent-based systems, model lifecycle tooling, and responsible AI practices preferred.

Compensation: Not specified
City: Palo Alto
Country: United States

Full Job Description

Location: Palo Alto, CA, United States

Join JPMorganChase, where you can help shape how applied artificial intelligence and machine learning accelerate decision-making, improve efficiency, and unlock new client value. You will partner with product, engineering, and business leaders to build reusable agent platform capabilities that are reliable, scalable, and responsible.

As an Applied Artificial Intelligence and Machine Learning Lead at JPMorganChase within our agent platform team, you will drive the delivery of production-ready agent capabilities and developer tooling that enable teams to safely build and operate agent-based solutions. You will set technical direction, raise engineering standards, and influence architecture decisions that balance speed, risk, and long-term maintainability. You will also mentor engineers and practitioners while collaborating across teams to align platform outcomes to measurable business impact.

Job responsibilities

Define and drive the platform roadmap for agent-based capabilities, focusing on measurable outcomes, reliability, and usability

Lead end-to-end delivery of core agent platform components, including software development kits, reference implementations, and integration patterns

Partner with product, engineering, risk, and control stakeholders to align requirements, prioritize trade-offs, and unblock execution

Establish quality, performance, and operational standards for agent workloads, including monitoring, testing, and incident readiness

Translate experimentation into production by driving clear architecture decisions, scalable designs, and repeatable deployment practices

Guide responsible development practices by embedding governance, privacy, and model risk considerations into platform design

Mentor and develop team members through technical coaching, design reviews, and continuous improvement of engineering practices

Communicate technical strategy and progress to senior stakeholders with clarity, data, and pragmatic recommendations

Required qualifications, capabilities and skills

Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experience

Demonstrated experience building and operating production software systems that integrate machine learning capabilities

Strong programming skills in at least one modern language (for example, Python, Java, or Go) and experience with modern software engineering practices

Hands-on experience with machine learning frameworks (for example, PyTorch, TensorFlow, or JAX) and model lifecycle tooling

Experience designing platforms or shared services used by multiple teams, including clear interfaces, documentation, and developer experience focus

Working knowledge of cloud and container orchestration concepts (for example, Kubernetes) and performance or reliability engineering fundamentals

Proven ability to lead through influence, drive technical alignment, and deliver outcomes across cross-functional partners

Strong problem-solving skills, including ability to clarify ambiguity, evaluate trade-offs, and make sound technical decisions

Preferred qualifications, capabilities and skills

Experience building agent-based systems, orchestration patterns, or agent development tooling and evaluation frameworks

Experience designing scalable inference or model serving architectures, including latency, throughput, and cost optimization

Familiarity with responsible artificial intelligence practices, model risk concepts, and governance-by-design approaches

Experience contributing to or maintaining widely used open-source software in machine learning or infrastructure ecosystems

Domain knowledge applying machine learning to regulated financial services use cases

FEDERAL DEPOSIT INSURANCE ACT: This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChases review of criminal conviction history, including pretrial diversions or program entries.

Lead applied AI and machine learning platforms that deliver secure, scalable agent-based solutions.

Full Job Description