Jersey City, NJ, United States
Live, first seen by us 16 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Design and ship production AI agents, owning them from prototype through production
Build Graph RAG retrieval combining knowledge-graph traversal with vector search
Design episodic and semantic agent memory with recall, summarization, and decay policies
Compose multi-agent workflows using A2A and integrate tools through MCP servers
Build and run agent evals, regression suites, and quality safety gating before release
Design and ship production agents on NEO across the federated portfolio, owning them from prototype through 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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- 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.
- 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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New AI Engineer postings at the employers we follow are up 134% over the last 6 weeks compared with the 6 before.
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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
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