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
Skills Required
- MS in Computer Science, Statistics, Mathematics, Machine Learning, or a related field, or equivalent experience
- Production experience building LLM-powered or agentic applications, including tracing, evaluations, and guardrails
- Strong Python programming skills and knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics
- Knowledge of Kubernetes, including AWS EKS
- Experience training models in Databricks and SageMaker
- Experience working with MLflow
- Practical RAG experience with retrieval quality, embeddings, and vector stores
- Expert knowledge of at least one of AWS, Azure, or Kubernetes
- Knowledge of data management and data model design
- Experience with real-time processing using SQL and NoSQL stores such as PostgreSQL, OpenSearch, or Redis
- Excellent communication skills and ability to partner with senior technical and business stakeholders
- Experience with agent frameworks or runtimes, A2A, or MCP
- Experience designing agent memory and managing organizational context
- Knowledge of knowledge graphs and graph databases for retrieval
- Understanding of LLM fine-tuning and small language model inference
- Ability to develop full-stack products using modern JavaScript or TypeScript frameworks such as Next.js or Svelte
- Experience in the financial or payments domain at a large institution
JPMorganChase Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about JPMorganChase and has not been reviewed or approved by JPMorganChase.
-
Healthcare Strength — Medical, dental, vision, and mental-health coverage are broad, with wellness incentives, on-site or virtual care, and an EAP offering coaching and counseling. Plan materials emphasize accessible options, including multiple medical choices and tools to manage costs.
-
Parental & Family Support — Paid parental leave extends up to 16 weeks for all parents, supplemented by paid Critical Caregiver Leave. Family resources include backup childcare via Bright Horizons, lactation support and milk-shipping, family-building assistance, and even a free five-month SNOO rental for newborns.
-
Retirement Support — Retirement programs include a 401(k) with an annual company match and automatic pay credits for most employees, with a legacy pension available to earlier hires. An Employee Stock Purchase Plan at a 5% discount further supports long-term savings.
JPMorganChase Insights
What We Do
JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $3.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small businesses, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the world’s most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands. Technology fuels every aspect of our company and is at the heart of everything we do. With over 50,000 technologists globally and an annual tech spend of $12 billion, we are dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Learn more about technology at our firm, explore resources from our Distinguished Engineers, AI & ML researchers, and other experts; access the latest episode of our TechTrends podcast, and more at www.jpmorgan.com/technology. Information about JPMorgan Chase & Co. is available at www.jpmorganchase.com. ©2023 JPMorgan Chase & Co. All rights reserved. JPMorgan Chase is an Equal Opportunity Employer, including Disability/Veterans.
Why Work With Us
Our technologists work on a diverse range of solutions that include strategic technology initiatives, big data, mobile, electronic payments, machine learning, cybersecurity, enterprise cloud development, and other state-of-the-art technologies.
Gallery
.png){kind=logo}
