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 CIB and Payments to turn use cases into shipped, supported agents.
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience with working on AI/ML solutions.
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.
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.
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems
Preferred qualifications, capabilities, and skills
MS in Computer Science, Statistics, Mathematics, Machine Learning, or related field (or equivalent experience).
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. Graph RAG a strong plus.
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
