ml engineer in agentic commerce

J.P. Morgan is a global financial services provider offering strategic advice and products to corporations, governments, wealthy individuals, and institutional investors. Its Commercial & Investment Bank provides banking, markets, securities services, and payments, while offering strategic advice, raising capital, managing risk, and extending liquidity in markets worldwide.

• 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 by 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
• Build and run task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality and safety gating
• Deploy and operate solutions on public cloud with 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

• MS in Computer Science, Statistics, Mathematics, Machine Learning, or a related field, or equivalent experience
• Hands-on experience building production LLM-powered or agentic applications, including tracing, evaluations, and guardrails
• Strong Python programming skills and deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics
• Knowledge of Kubernetes (AWS EKS)
• Experience training models in Databricks and SageMaker
• Experience 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, and real-time processing using SQL and NoSQL stores
• Excellent communication skills and ability to partner effectively with senior technical and business stakeholders
• Будет плюсом: Graph RAG, agent frameworks or runtimes, A2A, MCP, agent memory design, organizational context management, knowledge graphs and graph databases for retrieval, LLM fine-tuning, small language model inference, full-stack development with modern JavaScript/TypeScript frameworks for agent UIs, experience in the financial or payments domain at a large institution

• Локация: Лондон