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