Job Responsibilities
Design and ship production agents on NEO across the federated portfolio, owning them from prototype through production.
Build retrieval that holds up in production: chunking, ranking, and grounding strategies that keep answers accurate and auditable.
Design agent memory: episodic and semantic memory organized as memory nodes, with recall, summarization, and decay policies tuned per use case.
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.
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 the Corporate & Investment Bank and Payments to turn use cases into shipped, supported agents.
Required Qualifications, Capabilities, and Skills
MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).
Minimum 7 years of development experience, with at least 4 years working on AI/ML solutions.
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.
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.
Preferred Qualifications, Capabilities, and Skills
Graph RAG experience (e.g., combining knowledge-graph traversal with vector search).
Experience with agent frameworks or runtimes, A2A, or MCP.
Agent memory design (memory nodes, episodic/semantic memory) and organizational context management.
Knowledge graphs and graph databases used for retrieval.
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).
Knowledge of high-performance languages such as Go or Rust.
