Applied AI ML Lead

JPMorgan Chase & Co.•Palo Alto, CA

4h•$171,000 - $260,000

About The Position

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.

Requirements

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

Nice To Haves

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

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