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
- Practical RAG experience—retrieval quality, embeddings, and vector stores; Graph RAG a strong plus
- Expert knowledge of at least one of: AWS, Azure, Kubernetes
- Knowledge of data management and data model design; real-time processing using SQL (e.g., Postgres) and NoSQL stores (e.g., OpenSearch, Redis)
- Excellent communication skills with the ability to partner effectively with senior technical and business stakeholders

Preferred Qualifications, Capabilities, and Skills:

- Experience with agent frameworks or runtimes, A2A, or MCP
- Agent memory design (memory nodes, episodic/semantic memory) and organizational context management
- Knowledge graphs and graph databases used for retrieval
- Understanding of LLM fine-tuning and small language model inference
- Ability to develop full-stack products using modern JavaScript/TypeScript frameworks (e.g., Next.js, Svelte) for agent UIs (AG-UI / NEO UI SDK)
- Experience working in the financial or payments domain at a large institution