Software Engineer GEN AI, Assistant Vice President

Job d:

Location:

Irving, Texas, United States Salary: $ - $Category:
Technology Company:
Citi 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 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 Git Hub, 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 trument 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.

Core Platform & Application Development Full-Stack Engineering & Microservices:
Lead the design, implementation, and lifecycle of custom AI platform components. Build production-grade, highly available microservices and full-stack applications using Python, Java (Spring Boot), and modern frontend frameworks (React) for internal LLM/SLM fine-tuning control planes, inference gateways, and experiment tracking dashboards.

Agentic Frameworks & Patterns:
Code, evaluate, and optimize multi-tenant intelligent agents using modular orchestration patterns such as ReAct (Reasoning + Acting) and ReWOO (Reasoning With Out Observation), leveraging frameworks like Google's Agent Development Kit (ADK), Lang Graph, and Lang Chain. Strict Decoupled Integration Layers:
Develop and enforce clean, decoupled enterprise integration architectures.

Software Engineering Fundamentals & Distributed Systems System Architecture & Design Patterns:
Apply clean architecture, domain-driven design (DDD), and SOLID principles across Python and Java codebases. Implement robust design patterns (Strategy, Factory, Adapter, Circuit Breaker, Saga) for modularity and fault isolation. Concurrency & High-Throughput Processing:
Write robust concurrent and asynchronous code (e.g., Python asyncio, multithreading, Java Completable Future/virtual threads, connection pooling) to handle streaming tokens, parallel agent tool execution, and high-concurrency LLM inference loops. Reliability, Fault Tolerance &…