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# Applied AI ML Lead - Machine Learning Engineer - Agentic Commerce

- **Company:** JPMorgan Chase & Co.
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Confluence, Microsoft Azure, Computer Programming, Continuous Delivery, Data Mining, Data Structures, Graph Database, Information Retrieval, Python (Programming Language), PostgreSQL, Machine Learning, NoSQL, Systems Development Life Cycle, Redis, Svelte, Next.js, Search Technologies, SQL Databases, TypeScript, Real Time Systems, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Snowflake, Multi-Agent Systems, Agentic-AI, Kubernetes, Information Technology, Data Analytics, Bitbucket, Data Management, Machine Learning Operations, Graph RAG, Splunk, Network Server, OpenSearch, AWS EKS, Databricks, Agent2Agent Protocol
- **Published:** October 2, 2026
- **Apply:** https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/requisitions/preview/210795877

## About the Role

* 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, * 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

## Description

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., * 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


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