Applied AI ML Lead

JPMorgan Chase & Co.; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorganChase seeks an Applied AI/ML Lead to drive production-ready agent platform capabilities and developer tooling. You will set technical direction, mentor engineers, and balance speed with risk and maintainability.

You will partner across product, engineering, risk, and control to align requirements and deliver measurable outcomes. The role emphasizes governance, privacy, and model risk considerations, with a focus on scalable architectures and end-to-end delivery.

Qualifications

• 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 (Python, Java, or Go) and experience with modern software engineering practices.
• Hands-on experience with machine learning frameworks (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 (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.

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.

Skills

AI/ML Experience

Production Systems

Programming (Python/Java/Go)

ML Frameworks (PyTorch, TensorFlow, JX

Platform Design

Cloud & Kubernetes

Leadership by Influence

Problem Solving

Tools

Kubernetes

Job description

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