Описание:
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.
Задачи:
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
Требования:
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
Условия:
Локация: Лондон
#J-18808-Ljbffr
ml engineer in agentic commerce in London employer: HireHi
TEAM LEWIS is an exceptional employer that fosters a collaborative and innovative work culture, making it an ideal place for Fullstack Developers to thrive. With a commitment to employee growth and development, the agency offers opportunities to work on diverse projects while contributing to meaningful social and environmental causes through the TEAM LEWIS Foundation. Located in a vibrant environment, employees benefit from a supportive team atmosphere that encourages creativity and excellence in all aspects of development.
