Agentic AI Engineer (AVP)

Citi • Tampa, FL

4d • $96,960 - $145,440 • Onsite

About The Position

The Agentic AI Engineer is a transformative professional operating at the intersection of cutting-edge artificial intelligence and enterprise-grade financial technology. This is not a role for those content with the status quo — it is a position for builders, innovators, and applied AI practitioners who are energized by the challenge of turning the immense promise of foundation models into reliable, real-world impact. As Citi continues to evolve its Controls Technology capabilities, this role sits at the heart of that ambition: architecting retrieval-grounded, context-aware, and increasingly autonomous AI systems that meet the exacting standards of one of the world's most complex financial institutions.
The successful candidate will work shoulder-to-shoulder with senior developers, AI architects, and business stakeholders across Citi's global organization, driving the development of agentic AI applications that are not only technically sophisticated but genuinely transformative. From engineering robust RAG pipelines and knowledge graphs to designing multi-agent workflows and deploying production-grade GenAI applications, this individual will be a driving force behind solutions that enhance operational integrity, accelerate decision-making, and position Citi at the forefront of responsible AI adoption. The impact of your work will extend far beyond lines of code — it will be felt across teams, products, and ultimately, the clients and communities Citi serves around the world.

Requirements

• 3–5 years of professional experience in software or AI development, with hands-on exposure to Generative AI and agentic AI.
• Demonstrated experience delivering AI/GenAI projects in a collaborative team environment.
• Exposure to cloud platforms and services.
• Proficiency in Python for GenAI development, data preprocessing, and scripting.
• Solid understanding of core generative AI concepts — foundation models, LLMs, tokenization, embeddings, and context windows.
• Hands-on experience with prompt engineering and context engineering techniques.
• Experience building RAG systems, including chunking strategies, vector databases, and hybrid search techniques.
• Familiarity with knowledge graphs and an interest in Graph RAG for relationship-aware, multi-hop retrieval.
• Exposure to agentic AI development — building tool-using agents and multi-step workflows with a framework such as Google ADK, LangGraph, CrewAI, or the OpenAI Agents SDK.
• Working knowledge of agent tooling and protocols, including tool/function calling and the Model Context Protocol (MCP); awareness of the A2A protocol for inter-agent communication.
• Understanding of agent harness basics — session/state management, memory, guardrails, and execution controls that make agents reliable.
• Practical experience consuming major GenAI APIs (e.g., OpenAI, Gemini, Claude) and orchestration frameworks such as LangChain and LlamaIndex.
• Understanding of application deployment and containerization (Docker).
• Understanding of version control systems (Git).
• Awareness of AI compliance, data privacy, guardrails, and responsible AI principles.
• Strong teamwork and communication abilities.
• Willingness to learn new AI/GenAI and agentic technologies and frameworks.
• Analytical mindset and attention to detail.
• Openness to feedback and continuous improvement.

Responsibilities

• Build and integrate generative AI applications using pre-trained and hosted foundation models (via managed GenAI APIs and open-model endpoints).
• Design and implement context engineering workflows — assembling system instructions, retrieved knowledge, tool definitions, conversation memory, and task metadata into reliable, token-efficient prompts.
• Contribute to prompt engineering (zero-shot, few-shot, chain-of-thought, role-based prompting) and the development of AI-powered workflows.
• Develop and maintain Retrieval-Augmented Generation (RAG) systems, including chunking, embedding, hybrid (semantic + keyword) search, and re-ranking.
• Assist with building knowledge graphs and Graph RAG pipelines to support multi-hop reasoning and grounded, traceable responses.
• Contribute to agentic workflows — building AI agents with tool-calling, structured planning, and memory, using frameworks such as Google Agent Development Kit (ADK), LangGraph, or CrewAI.
• Integrate agents with external tools and data sources via the Model Context Protocol (MCP), and enable agent-to-agent collaboration via the Agent2Agent (A2A) protocol under guidance.
• Assist with the deployment, monitoring, and maintenance of GenAI and agentic applications in production environments.
• Collaborate with data scientists and engineers to ensure seamless integration of AI capabilities.
• Perform data preprocessing, document ingestion, and API development for AI applications.
• Participate in code reviews, testing, and documentation to ensure quality and reliability.
• Stay updated with advancements in GenAI and agentic AI, and share relevant learnings with the team.

Benefits

• medical, dental & vision coverage
• 401(k)
• life, accident, and disability insurance
• wellness programs
• paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level