Reads like an AI Engineer role: building products on existing AI models.
Jersey City, NJ, United States
Live, first seen by us 39 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Design and ship production LLM agents on NEO, from prototype to operation
Build Graph RAG retrieval combining knowledge-graph traversal with vector search
Design agent memory with recall, summarization, and decay policies
Compose multi-agent workflows using A2A and integrate tools via MCP servers
Build and run agent evals, regression suites, and quality and safety gating
Design and ship production agents on NEO across the federated portfolio, owning them from prototype through production.
Build retrieval that holds up in production: Graph RAG combining knowledge-graph traversal with vector search
Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case.
Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers
Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality/safety gating before release.
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Day 39
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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 (Investment Banking, Markets, Securities Services, or adjacent).
- Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced in one or more programming language(s) - Strong programming skills in Python, with deep knowledge of data structures, algorithms, machine learning, data mining, information retrieval, and statistics. - Hands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails.
- Experience working in the financial or payments domain at a large institution (Investment Banking, Markets, Securities Services, or adjacent). - Knowledge of high-performance languages such as Go or Rust
- Deep proficiency with Kubernetes and Amazon EKS, micro-VM isolation (e.g., Firecracker, Kata Containers, gVisor), and sidecar architectures, with proven experience designing defense in depth across the stack: network and mTLS, workload identity, fine-grained authorization, runtime isolation for untrusted or adversarial workloads, and application-level guardrails - Practical RAG experience — retrieval quality, embeddings, and vector stores; Expert knowledge of at least one of: AWS, Azure, Kubernetes. Knowledge of data management and data model design; real-time processing using both 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.
As a Software Engineer III at JPMorganChase within the Commercial and Investment bank Digital & Platform Services, Data Analytics, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. JPMorganChase is hiring top talent to join the growing Commercial & Investment Bank Technology organization within Digital & Platform Services / Data Analytics, building production AI agents on NEO that leverage the firm’s scale, data, and full-service advantage to deliver measurable impact across the Commercial & Investment Bank and Payments. As a Senior AI Application Engineer, you’ll design, productionize, and operate LLM-powered agents on NEO (the firm’s agent runtime PaaS on AWS/Azure), partnering closely with business, product, and engineering teams in a fast-paced environment. Job responsibilities
- Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality/safety gating before release. - Deploy and operate solutions on public cloud (AWS and, or Azure) with strong SDLC, security, resiliency, and observability practices. - Partner with product and business partners across CIB and Payments to turn use cases into shipped, supported agents.
- Own organizational context management — assembling entitlement-, lineage-, and tenant-aware context so each agent reasons over only what it’s allowed to see. - Compose multi-agent workflows using A2A, and integrate tools and data through MCP servers (Bitbucket, Confluence, Databricks, Kubernetes, Snowflake, Splunk). - Build and run evals: task-level and end-to-end agent evaluations, regression suites, LLM-as-judge, and quality/safety gating before release.
- Hands-on experience building LLM-powered or agentic applications in production, including tracing, evaluations, and guardrails. - Deep proficiency with Kubernetes and Amazon EKS, micro-VM isolation (e.g., Firecracker, Kata Containers, gVisor), and sidecar architectures, with proven experience designing defense in depth across the stack: network and mTLS, workload identity, fine-grained authorization, runtime isolation for untrusted or adversarial workloads, and application-level guardrails - Practical RAG experience — retrieval quality, embeddings, and vector stores; Expert knowledge of at least one of: AWS, Azure, Kubernetes. Knowledge of data management and data model design; real-time processing using both SQL (e.g., Postgres) and NoSQL stores (e.g., OpenSearch, Redis).
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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.
In-depth knowledge of the financial services industry and their IT systems
Preferred qualifications, capabilities, and skills
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Jersey City, NJ, United States
Working pattern and location restrictions need checking in the full posting.
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