Job description

About Citi Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management. As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services. Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We ll enable growth and progress together. About the Team 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. 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. Qualifications 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 . Required Technical Skills 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. Required Soft Skills 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. Education Bachelor s degree or Master s degree in Computer Science, Data Science, AI, or a related field. ------------------------------------------------------ Job Family Group: Technology ------------------------------------------------------ Job Family: Applications Development ------------------------------------------------------ Time Type: Full time ------------------------------------------------------ Primary Location: Tampa Florida United States ------------------------------------------------------ Primary Location Full Time Salary Range: $96,960.00 - $145,440.00 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. ------------------------------------------------------ Other Relevant Skills For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------ Anticipated Posting Close Date: Sep 21, 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. Illinois residents – AI Notice and Right ------------------------------------------------------ Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi . View Citi’s EEO Policy Statement and the Know Your Rights poster.

Requirements

Must-haves

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.
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 .
Required Technical Skills 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 .

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aiapipythondockeraccessibility

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Wellness