Lead Machine Learning Engineer - Agentic Pricing

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

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.

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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.

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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.

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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.

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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.