Applied AI ML Lead - Machine Learning Engineer - Agentic Commerce

Join us to shape the future of AI-powered solutions at JPMorganChase. You’ll leverage the firm’s scale, data, and technology to deliver measurable impact across the Commercial & Investment Bank and Payments. As a Lead AI and ML Engineer, you’ll collaborate with talented teams in a fast-paced environment, building agents that real businesses depend on. We offer opportunities for career growth, exposure to cutting-edge platforms, and the chance to make a difference in a regulated, secure setting.

As a Lead AI and ML Engineer in Digital & Platform Services / Data Analytics, you will design, productionize, and operate LLM-powered Agentic Commerce B2B agents on NEO. You will apply MLOps for automation, continuous delivery, and compliance, turning innovative ideas into shipped, production-grade agents. You’ll partner closely with business, product, data science, and engineering teams, expanding NEO’s portfolio of production agents across CIB sub-LOBs and Payments. Your work will help drive secure, auditable, and impactful AI solutions.

Job Responsibilities:

• Design and ship production agents on NEO, owning them from prototype through production
• Build robust retrieval systems using Graph RAG, knowledge-graph traversal, vector search, chunking, ranking, and grounding strategies
• Design agent memory, including episodic and semantic memory nodes, recall, summarization, and decay policies
• Manage organizational context, assembling entitlement-, lineage-, and tenant-aware context for secure agent reasoning
• Compose multi-agent workflows using A2A and integrate tools and data through MCP servers (Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, Splunk)
• Build and run task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality/safety gating
• Deploy and operate solutions on public cloud (AWS and/or Azure) with strong SDLC, security, resiliency, and observability practices
• Partner with product and business teams to turn use cases into shipped, supported agents
• Build traditional ML model training pipelines and productionize them using MLOps best practices
• Develop batch and online inference for ML models

Required Qualifications, Capabilities, and Skills:

• MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience)
• Hands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails
• Strong programming skills in Python, with deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics
• Knowledge of Kubernetes (AWS EKS)
• Experience with training models in Databricks and SageMaker
• Experience working with MLFlow