**Principal Research Engineer - AI/ML** drives next-gen AI for banking. Architect end-to-end agent-based and generative AI solutions, from research to production. Manage cross-functional teams, ensuring reliability, security, and scalability. Requires 10+ years in AI/ML, advanced degrees in related fields, and enterprise-scale deployment experience. Mentee engineers and researchers to raise technical standards. Preferred: AWS ML experience, open-source contributions, domain expertise in financial services. Senior-level role at JPMorganChase in Jersey City, NJ.
Full Job Description
Location: Jersey City, NJ, United States
Our goal is to build the next generation of AI: autonomous agents that can reason, plan, act, and learn to solve critical problems for an industry leading financial institution. We are looking for architects who will define the future of banking through Agentic AI. The Applied Artificial Intelligence and Machine Learning team in Commercial and Investment Banking is transforming operations by leveraging the latest advancements in agentic AI and frontier models.
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 workflowsTranslate ambiguous business problems into research hypotheses, measurable success metrics, and production-ready designsBuild and ship multiple collaborating agents that coordinate planning and execution across large, multi-step processesDesign reusable services, libraries, and evaluation frameworks that accelerate adoption across artificial intelligence and engineering teamsEstablish robust experimentation practices, including offline/online evaluation, monitoring, and iterative improvement loopsPartner with stakeholders across teams to identify priority use cases, define roadmaps, and deliver scalable capabilitiesEnsure solutions meet enterprise expectations for reliability, security, and long-term maintainability in productionMentor and coach engineers and researchers through design reviews, technical guidance, and knowledge sharingRequired qualifications, capabilities and skills
Formal training or certification on applied artificial intelligence and machine learning concepts and 10+ years applied experienceAdvanced degree (masters or doctorate) in computer science, engineering, statistics, or a related quantitative discipline, or equivalent practical experienceDemonstrated experience deploying machine learning and/or generative artificial intelligence systems into production at enterprise scaleStrong foundation in machine learning fundamentals, experimental design, and data-driven decision-makingExperience designing distributed systems for model training, inference, and stateful services in production environmentsProven ability to create evaluation strategies for agent-based systems (quality, safety, latency, cost, and reliability), and improve them over timeStrong programming and engineering skills with a track record of building maintainable, reusable components used by other teamsDemonstrated ability to lead through influence in cross-functional environments and drive alignment across technical and non-technical stakeholdersPreferred 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 agentsExperience 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 useDomain experience applying advanced analytics or artificial intelligence to large-scale operational processes in financial services or other regulated industries#LI-RB1
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Build autonomous AI agents from research to production to transform banking operations at enterprise scale.
