Gen AI Engineer: Multi-Agent AI & Production Systems

Citi; Irving (TX); On-site; USD 107,000 - 161,000; Full time

Benefits offered by this job

Medical benefits

401(k)

Paid time off

Job summary

Citi is the world’s most global bank, investing in scalable AI to securely support a complex financial infrastructure. The USCC Architecture and AI Engineering team owns an enterprise multi-agent platform that autonomously supports the software delivery lifecycle, from product analysis to deployed code.

The role focuses on building production AI agents, integrating LLMs, and crafting robust pipelines with observability, CI/CD, and secure coding practices.

Qualifications

• 3 to 7 years of hands-on software engineering experience with a track record of shipping production software.
• Strong CS 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.

Responsibilities

• 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 ADK, including sub-agent orchestration, human-in-the-loop approval gates, and agent-to-agent messaging.
• Integrate Gemini and Anthropic Claude into agent pipelines via enterprise gateways, applying advanced prompt engineering and structured tool 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).

Skills

Python

LLM development

Multi-agent frameworks

Algorithms

Databases

Go

React/TypeScript

CS fundamentals

Education

Bachelor's degree in Computer Science

Tools

Docker

Kubernetes/OpenShift

MCP

GitHub

JIRA

Langfuse

Prometheus

Job description

Citi is the world’s most global bank, investing in scalable AI to securely support a complex financial infrastructure. The USCC Architecture and AI Engineering team owns an enterprise multi-agent platform that autonomously supports the software delivery lifecycle, from product analysis to deployed code.

The role focuses on building production AI agents, integrating LLMs, and crafting robust pipelines with observability, CI/CD, and secure coding practices.