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.

Responsibilities

• Build and maintainproduction 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.

Education:

• Bachelor's degree in Computer Science, or a related field.