Lead Machine Learning Engineer - Agentic Pricing

Job Summary JPMorganChase is hiring top talent to join the growing Corporate & Investment Bank Technology organization within Digital & Platform Services / Data Analytics , building production AI agents on NEO that leverage the firm’s scale, data, and full-service advantage to deliver measurable impact across the Commercial & Investment Bank and Payments .

As a Senior AI Application Engineer within the firm’s NEO agent runtime platform on AWS and Azure , you will design, productionize, and operate large language model (LLM)-powered agents , partnering closely with business, product, and engineering teams in a fast-paced environment.

Core Selling Aspects (Why this role) You’ll build and ship agents that real businesses depend on, not demos. NEO already runs a federated portfolio of production agents — forecasting, anomaly detection, log analysis, with sales fulfillment and voice-of-client close behind — and you’ll add to it.

You’ll work across Corporate & Investment Bank sub–lines of business and Payments, using NEO’s runtime, retrieval, and memory primitives plus platforms such as Databricks and the GenAI Gateway, and apply MLOps for automation, continuous delivery, and compliance with AI/ML control expectations.

NEO is the firm’s agent runtime: it gives agents secure execution, agent-to-agent (A2A) communication, MCP-based tool access, a managed memory layer, and permission-aware, auditable operation in a regulated environment. Your job is to turn that platform into shipped agents.

Job Responsibilities

• Design and ship production agents on NEO across the federated portfolio, owning them from prototype through production.
• Build retrieval that holds up in production: chunking, ranking, and grounding strategies that keep answers accurate and auditable.
• Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case.
• Own organizational context management — assembling entitlement-, lineage-, and tenant-aware context so each agent reasons over only what it’s allowed to see.
• Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers (Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, Splunk).
• Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality/safety gating before release.
• Deploy and operate solutions on public cloud (AWS and/or Azure) with strong SDLC, security, resiliency, and observability practices.
• Partner with product and business partners across the Corporate & Investment Bank and Payments to turn use cases into shipped, supported agents.

Required Qualifications, Capabilities, and Skills

• MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).
• Minimum 7 years of development experience, with at least 4 years working on AI/ML solutions.
• 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.
• Practical RAG experience — retrieval quality, embeddings, and vector stores.
• Expert knowledge of at least one of: AWS, Azure, Kubernetes.
• Knowledge of data management and data model design; real-time processing using both 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

• Graph RAG experience (e.g., combining knowledge-graph traversal with vector search).
• 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 (Investment Banking, Markets, Securities Services, or adjacent).
• Knowledge of high-performance languages such as Go or Rust.

About Us JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color,