What are the responsibilities and job description for the AI Agents Applied Research/Engineering Lead - Vice President position at JPMorgan Chase?
Transform how millions of customers manage their money, make decisions, and get more from their financial relationships through a human-centered approach that blends cutting-edge AI with clear, trustworthy experiences.
Chase serves over 80 million customers and is building the next generation of conversational AI to power personalized financial decision-making across travel, banking, lifestyle services, and more. We're looking for an AI Agents Applied Research/Engineering Lead to drive the research, design, and deployment of agentic AI systems at the heart of that effort.
As AI Agents Applied Research/Engineering Lead, you will work with the team to shape how millions of customers discover, decide, and act—turning multi-step financial tasks into simple conversations. You'll lead the end-to-end lifecycle of LLM-based agents: defining research directions in areas like multi-step planning, tool use, and safety; building production systems that perform under real-world latency, accuracy, and compliance constraints; and partnering with Product, Engineering, Design, and Risk teams to bring those systems to market. The problems here are genuinely unusual—building AI that must be not just accurate but auditable, explainable, and safe in a highly regulated, high-stakes domain.
You'll have the opportunity to publish at top-tier venues like NeurIPS, ICML, and ACL—and see that research deployed to a user base of over 80 million customers.
Job Responsibilities
Day to day, you'll operate across the full stack—from research to production:
Lead research and deployment of agentic AI systems with multi-step workflows, tool calling, and multi-agent orchestration.
Fine-tune and optimize LLMs using parameter-efficient fine-tuning (PEFT), distillation, and quantization to meet production constraints such as latency, memory, and cost.
Apply reinforcement learning and preference optimization to improve personalization and dialogue policies.
Scale LLM systems through caching, batching, prompt governance, and evaluation frameworks.
Implement privacy, safety, and security controls including PCI compliance, jailbreak resistance, and auditability.
Design rigorous experiments with strong baselines and meaningful metrics.
Define and track success metrics for agent performance, including task completion rate, accuracy, latency, and customer satisfaction.
Required Qualifications, Capabilities, and Skills
Ph.D. with 1 years or M.S. with 3 years building and deploying AI systems in production
Applied GenAI experience with LLMs including fine-tuning, prompt engineering, and RAG.
Experience scaling LLM systems with caching, batching, governance, and evaluation.
Strong foundation in ML, deep learning, statistical modeling, and experimental design.
Experience in Information Retrieval (indexing, ranking, retrieval) and/or recommendation systems.
Proficiency in Python and ML frameworks (PyTorch/TensorFlow, Hugging Face, scikit-learn)
Demonstrated ability to set a technical research agenda and drive it from concept through production deployment.
Experience presenting research findings and technical strategy to senior leadership and non-technical stakeholders.
Preferred Qualifications, Capabilities, and Skills
5 years developing conversational AI systems, virtual assistants or LLM-based systems in production.
Experience with multi-agent orchestration, supervisor agents, and specialized toolkits.
Expertise in agent governance, red-teaming, adversarial testing, and safety evaluation.
Experience with reinforcement learning, bandit algorithms, and preference-based optimization (DPO, IPO), with practical exposure to data collection, labeling, and evaluation pipelines.
MLOps/LLMOps experience with CI/CD, monitoring, versioning, A/B testing, and rollbacks.
Track record of data-driven product development and experimentation.
Publications in top-tier AI/ML venues and/or open-source contributions