Lead Agentic AI Engineer – Vice President

Citi is seeking a Lead Agentic AI Engineer at the Vice President level to architect and deliver production-grade agentic AI systems that sit at the intersection of large language models, retrieval-augmented generation, and modern full-stack engineering. This role operates within Citi's technology organization and carries direct ownership over the design, development, and deployment of intelligent, multi-agent platforms that drive measurable outcomes across the business.

You will build and scale multi-tenant agentic ecosystems, develop custom fine-tuning applications, and ensure the platform adheres to our core principles:
Trust, Adoption, Cost, Operations, and Scalability. You will lead by example, setting the standard for code quality and architectural purity while driving high-impact business use cases.

This role requires deep expertise in Python, LLMs, and Agentic workflows, combined with strong full-stack capabilities (Java, React, and microservices) to seamlessly integrate AI capabilities into enterprise-grade platforms.

• Core Platform & Application Development Full-Stack Engineering:
  Lead the development of custom AI platform components. Build and maintain full-stack applications utilizing Python, Java (Spring Boot), and modern frontend frameworks like React (e.g., developing internal LLM/SLM fine-tuning planes, microservices, and experiment tracking dashboards). Agentic Frameworks:
  Code and optimize multi-tenant intelligent agents utilizing modular orchestration patterns such as ReAct and ReWOO, and frameworks like Google's Agent Development Kit (ADK). Strict Architectural Implementation:
  Develop and enforce clean, decoupled integration layers. Build Model Context Protocol (MCP) servers ensuring a strict communication flow:
  Agents interact solely with MCP servers, and MCP servers interact solely with APIs to retrieve data. Direct database access from agents or MCP servers is strictly prohibited.
• Advanced Data Retrieval & Logic Engineering Next-Generation RAG:
  Write the data ingestion and retrieval code for advanced Retrieval-Augmented Generation (RAG) architectures, including Knowledge Graph RAG (GraphRAG), LightRAG, and hierarchical summary trees (RAPTOR). Vector & Graph Integrations:
  Develop seamless integrations with graph and vector databases (such as Neo4j and pgvector) to power complex, thematic data retrieval. Prompt & Intent Engineering:
  Design robust LLM instructions and classification logic to prevent collisions in complex workflows, ensuring mutually exclusive intents are handled with high precision.
• Use Case Development & Forward Deployment SME
  
  Collaboration:
  
  Act as a Forward Deployed Engineer (FDE), working directly with Subject Matter Experts (SMEs) on business and domain understanding to accurately translate complex enterprise workflows into automated, agent-driven code. Seamless Integration:
  Partner with UI and workflow integration developers to ensure the backend agentic logic connects flawlessly with user-facing layers and existing enterprise APIs.

• 10+ years of professional software engineering experience.
• 5+ years of experience in AI/ML, with at least 2+ years specifically in Generative AI (LLMs, RAG, Agentic workflows).
• 3+ years of leadership experience managing technical teams and delivering complex software or AI solutions.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.

• AI/ML & Python:
  Deep hands‑on expertise in Python, LLM orchestration, prompt engineering, and agentic frameworks (ADK, Lang Chain, Lang Graph, etc.).
• Full‑Stack & Microservices:
  Strong proficiency in backend development using Java (Spring Boot) or Python (FastAPI), and frontend development using React.
• Dat…