As a Applied AI Machine Learning Director at JPMorganChase within the Applied AI Research team in the Commercial & Investment Bank, you will lead the design and delivery of agent-based and generative artificial intelligence solutions that transform complex operations. You will bridge state-of-the-art research and enterprise-grade engineering to build systems that are safe, reliable, and scalable. You will partner across product, engineering, and business teams to prioritize high-impact problems and deliver outcomes. You will also raise the technical bar through mentorship, technical leadership, and strong scientific rigor.

Job responsibilities

• Architect end-to-end agent-based and generative artificial intelligence solutions to automate complex operational workflows
• Translate ambiguous business problems into research hypotheses, measurable success metrics, and production-ready designs
• Build and ship multiple collaborating agents that coordinate planning and execution across large, multi-step processes
• Design reusable services, libraries, and evaluation frameworks that accelerate adoption across artificial intelligence and engineering teams
• Establish robust experimentation practices, including offline/online evaluation, monitoring, and iterative improvement loops
• Partner with stakeholders across teams to identify priority use cases, define roadmaps, and deliver scalable capabilities
• Ensure solutions meet enterprise expectations for reliability, security, and long-term maintainability in production
• Mentor and coach engineers and researchers through design reviews, technical guidance, and knowledge sharing

Required qualifications, capabilities and skills

• Formal training or certification on applied artificial intelligence and machine learning concepts and 10+ years applied experience
• Advanced degree (master’s or doctorate) in computer science, engineering, statistics, or a related quantitative discipline, or equivalent practical experience
• Demonstrated experience deploying machine learning and/or generative artificial intelligence systems into production at enterprise scale
• Strong foundation in machine learning fundamentals, experimental design, and data-driven decision-making
• Experience designing distributed systems for model training, inference, and stateful services in production environments
• Proven ability to create evaluation strategies for agent-based systems (quality, safety, latency, cost, and reliability), and improve them over time
• Strong programming and engineering skills with a track record of building maintainable, reusable components used by other teams
• Demonstrated ability to lead through influence in cross-functional environments and drive alignment across technical and non-technical stakeholders

Preferred qualifications, capabilities and skills

• Experience deploying and operating machine learning workloads on Amazon Web Services (for example, Amazon SageMaker or Amazon Bedrock)
• Publication history, open-source contributions, or demonstrated applied research impact in areas such as large language models, reinforcement learning, or autonomous agents
• Experience with containerized and cloud-native deployment patterns (for example, Kubernetes-based platforms)
• Familiarity with governance and risk considerations for artificial intelligence systems, including privacy, model safety, and responsible use
• Domain experience applying advanced analytics or artificial intelligence to large-scale operational processes in financial services or other regulated industries

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