Reads like an AI Engineer role: building products on existing AI models.
London, London, United Kingdom
Live, first seen by us 43 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Design and ship production agents on NEO, from prototype through production
Build retrieval systems using Graph RAG, vector search, and grounding strategies
Compose multi-agent workflows using A2A and integrate tools through MCP servers
Build and run agent evaluations, regression suites, and quality/safety gating
Build ML training pipelines and batch and online inference using MLOps practices
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
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/safety gating
Build traditional ML model training pipelines and productionize them using MLOps best practices
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Day 43
Similar jobs usually come down between day 4 and day 77.
Well past JPMorgan Chase’s usual: its jobs come down by about day 15.
From 51 similar postings that came down, and 1,000 of JPMorgan Chase’s. Coming down is not always a hire.
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- 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
- 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)
- 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
- 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
- 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
- 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)
- 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
- Knowledge of Kubernetes (AWS EKS) - Experience with training models in Databricks and SageMaker - Experience working with MLFlow
- 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
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We file this job under AI Engineer, by the work the posting describes.
AI engineers build products on top of AI models that already exist, most often large language models: they connect a model to a company's data and tools and ship the assistants, agents and search features people use.
Postings ask for strong Python or TypeScript, experience with model APIs, retrieval and agent frameworks, and ways to test whether the model's answers are good enough.
An ML engineer trains and serves models; an AI engineer usually starts from a model someone else trained and builds the product around it, though titles often mix the two.
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.
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London, London, United Kingdom
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