Software Engineer GEN AI, Assistant Vice President

Citi is the world's most global bank, serving clients in more than 160 countries. Citi is investing heavily in scaled, enterprise-grade generative and agentic AI that operates securely and reliably across a highly regulated financial infrastructure. You'll join the USCC Architecture and AI Engineering team, which owns an enterprise multi-agent AI platform that autonomously supports the software delivery lifecycle, from product analysis through to reviewed, deployable code.       As a Generative AI Engineer (AVP), you will build and scale production components of this platform. This is a hands-on individual contributor role for an engineer with a strong foundation in computer science, software engineering, and applied AI, ready to ship real production systems rather than one-off prototypes.

Build and maintain production AI agents covering product analysis, requirements capture, UI prototyping, backlog generation, and automated code implementation with pull request creation.   Implement multi-agent workflows using the Google Agent Development Kit (ADK), including sub-agent orchestration, human-in-the-loop approval gates, and agent-to-agent (A2A) messaging.   Integrate Gemini and Anthropic Claude into agent pipelines via enterprise gateways, applying advanced prompt engineering and structured tool/function calling.   Connect agents to enterprise systems, including Engineering & AI Product Documentation, JIRA, and GitHub, using MCP-style tool interfaces.   Build RAG pipelines using vector search, embeddings, and clustering to ground agent responses in accurate enterprise context.   Build evaluation harnesses (LLM-as-judge, parallel jury methods) to measure faithfulness, relevancy, hallucination rate, and task completion.   Write advanced, async Python and build well-documented FastAPI services, backed by persistent PostgreSQL session state.   Apply data structures, algorithms, and complexity analysis to keep orchestration and retrieval logic efficient at scale.   Instrument services for observability, maintain CI/CD and containerized deployments, apply secure coding and guardrail practices, and write tests (unit, integration, end-to-end) as part of every change.       Required Qualifications & Skills   3 to 7 years of hands-on software engineering experience (5+ for PhD holders), with a track record of shipping production software.   Strong computer science fundamentals: data structures, algorithms and complexity, operating systems, networking, and relational database design/optimization.   Advanced Python (async, typing, dependency management) and solid software engineering practices: clean code, automated testing, version control, and CI/CD.   Hands-on experience with LLM application development (prompt engineering, tool/function calling, structured outputs) and multi-agent/agentic frameworks.   Practical RAG pipeline experience using vector databases and embedding-based retrieval, plus familiarity with structured LLM/agent output evaluation methods.   Experience integrating AI systems with external tools/documentation via structured interfaces (e.g., MCP ), and working knowledge of Docker and Kubernetes/OpenShift.       Beneficial Skills & Qualifications   Direct experience with Google ADK or comparable orchestration frameworks; observability tooling ( Langfuse , Prometheus, distributed tracing).   Exposure to Go, and front-end development with React/TypeScript (including SSE streaming).   Experience with ChromaDB / pgvector , HDBSCAN clustering, HashiCorp Vault, and SSO/JWT authentication patterns.   Experience with GitHub, JIRA, and documentation-search integrations for backlog and pull request workflows.

Bachelor's degree in Computer Science, or a related field. ------------------------------------------------------ Job Family Group: Technology ------------------------------------------------------ Job Family: Digital Software Engineering