JPMorgan Chase
AI Agents Applied Research/Engineering Lead - Vice President
$138K–$260K
New York, NY +2 · Director · Full-time · Posted Apr 7, 2026
- Location
- New York, NY
Wilmington, DE
Palo Alto, CA - Experience
- Ph.D. with 1+ years or M.S. with 3+ years building and deploying AI systems in production; preferred: 5+ years developing conversational AI, virtual assistants, or LLM-based systems in production.
- Education
- Ph.D. or M.S.
About the job
Leads research and production deployment of LLM-based agents for customer-facing financial tasks, emphasizing performance, safety, auditability, and compliance.
What you'll do
- Lead research and deployment of agents using multi-step workflows, tool calling, and multi-agent orchestration.
- Fine-tune and optimize LLMs and scale them for production constraints; apply reinforcement learning and preference optimization.
- Implement privacy, safety, and security controls, design experiments, and track agent performance metrics.
What they ask for
- 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.
- Strong foundation in ML, deep learning, statistical modeling, and experimental design; experience in Information Retrieval and/or recommendation systems.
- Proficiency in Python and ML frameworks (PyTorch/TensorFlow, Hugging Face, scikit-learn); ability to lead research through production deployment.
Nice to have
- 5+ years developing conversational AI, virtual assistants, or production LLM-based systems.
- Experience with multi-agent orchestration, agent safety evaluation, reinforcement learning, MLOps/LLMOps, or top-tier AI/ML publications.
Pay
New York,NY $171,000.00-$260,000.00 USD base salary; Wilmington,DE $137,750.00-$235,000.00 USD base salary; Palo Alto,CA $171,000.00-$260,000.00 USD base salary
Tools
LLMs, PEFT, Python, PyTorch/TensorFlow, Hugging Face, scikit-learn, RAG, DPO
AI focus
Applied research and engineering for agentic AI in financial services, including multi-step planning, tool use, orchestration, LLM optimization, personalization, evaluation, and safety.
Model-written summary · the original posting is authoritative