**Applied AI ML Lead in Seattle, WA:** Lead development of production-ready agent capabilities, set technical direction, and mint talent. Key responsibilities include defining the platform roadmap, delivering core components, and establishing quality standards. Requires 5+ years of applied AI/ML experience, software engineering skills, and knowledge of ML frameworks. Drive outcomes across cross-functional teams with strong leadership skills.
Full Job Description
Location: Seattle, WA, 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 usabilityLead end-to-end delivery of core agent platform components, including software development kits, reference implementations, and integration patternsPartner with product, engineering, risk, and control stakeholders to align requirements, prioritize trade-offs, and unblock executionEstablish quality, performance, and operational standards for agent workloads, including monitoring, testing, and incident readinessTranslate experimentation into production by driving clear architecture decisions, scalable designs, and repeatable deployment practicesGuide responsible development practices by embedding governance, privacy, and model risk considerations into platform designMentor and develop team members through technical coaching, design reviews, and continuous improvement of engineering practicesCommunicate technical strategy and progress to senior stakeholders with clarity, data, and pragmatic recommendationsRequired qualifications, capabilities and skills
Formal training or certification on applied artificial intelligence and machine learning concepts and 5+ years applied experienceDemonstrated experience building and operating production software systems that integrate machine learning capabilitiesStrong programming skills in at least one modern language (for example, Python, Java, or Go) and experience with modern software engineering practicesHands-on experience with machine learning frameworks (for example, PyTorch, TensorFlow, or JAX) and model lifecycle toolingExperience designing platforms or shared services used by multiple teams, including clear interfaces, documentation, and developer experience focusWorking knowledge of cloud and container orchestration concepts (for example, Kubernetes) and performance or reliability engineering fundamentalsProven ability to lead through influence, drive technical alignment, and deliver outcomes across cross-functional partnersStrong problem-solving skills, including ability to clarify ambiguity, evaluate trade-offs, and make sound technical decisionsPreferred qualifications, capabilities and skills
Experience building agent-based systems, orchestration patterns, or agent development tooling and evaluation frameworksExperience designing scalable inference or model serving architectures, including latency, throughput, and cost optimizationFamiliarity with responsible artificial intelligence practices, model risk concepts, and governance-by-design approachesExperience contributing to or maintaining widely used open-source software in machine learning or infrastructure ecosystemsDomain knowledge applying machine learning to regulated financial services use cases
Lead applied AI and machine learning platforms that deliver secure, scalable agent-based solutions.
