Applied AI ML Lead - Machine Learning Engineer - Agentic Commerce

JPMorganChase; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

JPMorganChase is seeking a Lead AI and ML Engineer to design and ship production agents, build robust retrieval systems, and manage agent memory and context in a regulated, secure environment.

You will collaborate with teams across the bank, deploy on public clouds, and advance MLOps practices while delivering measurable business impact.

Qualifications

• MS in Computer Science, Statistics, Mathematics, 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.

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.

Skills

Python

Kubernetes (AWS EKS)

Databricks

SageMaker

ML/AI engineering

Distributed systems

Information retrieval

SQL/NoSQL

Education

MS in Computer Science

Tools

Databricks

SageMaker

OpenSearch

Snowflake

Bitbucket

Confluence

Job description

Job Description

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.

Job Description

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.

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

ABOUT US

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. 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, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

About The Team

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.