Software Engineer GEN AI, Assistant Vice President

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, 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.

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 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.

Job Family Group:

Technology

Job Family:

Digital Software Engineering

Time Type:

Full time

Primary Location:

Irving Texas United States

Primary Location Full Time Salary Range:

$107,120.00 - $160,680.00

In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.

Most Relevant Skills

Please see the requirements listed above.

Anticipated Posting Close Date:

Sep 25, 2026

Automated Processing and AI

We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.
Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision‑making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.

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