We are looking for an enthusiastic Generative AI Developer to join our Controls Technology team and support the development and integration of generative AI solutions. In this hands-on role, you will work under the guidance of senior developers and AI architects to help build retrieval-grounded, context-aware, and increasingly agentic AI applications. You will contribute to reliable, AI-driven features while growing your expertise across the modern GenAI stack. This role focuses on applying pre-trained and hosted foundation models — through context engineering, RAG, knowledge graphs, and agentic workflows — rather than training or fine-tuning models.
This is a growth-oriented role: you'll take ownership of well-scoped components, learn established patterns from senior engineers, and progressively increase your technical depth and independence.
Key Responsibilities- Assist in building and integrating generative AI applications using pre-trained and hosted foundation models (via managed GenAI APIs and open-model endpoints).
- Support the implementation of context engineering workflows — assembling system instructions, retrieved knowledge, tool definitions, and conversation memory into reliable, token-efficient prompts, following established patterns.
- Contribute to prompt engineering (zero-shot, few-shot, chain-of-thought, role-based prompting) for AI-powered workflows.
- Help develop and maintain Retrieval-Augmented Generation (RAG) components, including chunking, embedding, and semantic/keyword search.
- Support the development of knowledge graph and Graph RAG pipelines under guidance to enable grounded, traceable responses.
- Contribute to agentic workflows — helping build AI agents with tool-calling and basic planning/memory, using frameworks such as Google Agent Development Kit (ADK), LangGraph, or CrewAI.
- Assist with integrating agents to external tools and data sources via the Model Context Protocol (MCP), with exposure to the Agent2Agent (A2A) protocol.
- Support the deployment, monitoring, and maintenance of GenAI and agentic applications in production environments.
- Perform data preprocessing, document ingestion, and basic API development for AI applications.
- Collaborate with data scientists and engineers to integrate AI capabilities into products.
- Participate in code reviews, testing, and documentation to ensure quality and reliability.
- Stay curious about advancements in GenAI and agentic AI, and share learnings with the team.
- 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; foundational understanding of context engineering techniques.
- Practical experience (project or professional) building RAG systems, including chunking, vector databases, and semantic search.
- Familiarity with knowledge graphs and interest in Graph RAG for relationship-aware retrieval.
- Exposure to agentic AI development — building tool-using agents or multi-step workflows with a framework such as Google ADK, LangGraph, CrewAI, or the OpenAI Agents SDK.
- Awareness of agent tooling and protocols, including tool/function calling and the Model Context Protocol (MCP); familiarity with the A2A protocol is a plus.
- Basic understanding of agent harness concepts — session/state management, memory, and guardrails.
- Experience consuming major GenAI APIs (e.g., OpenAI, Gemini, Claude) and exposure to orchestration frameworks such as LangChain or LlamaIndex.
- Understanding of application deployment and containerization (Docker).
- Working knowledge of version control systems (Git).
- Awareness of AI compliance, data privacy, guardrails, and responsible AI principles.
- Strong teamwork and communication abilities.
- Eagerness to learn new AI/GenAI and agentic technologies and frameworks.
- Analytical mindset and attention to detail.
- Openness to feedback, coaching, and continuous improvement.
- Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
- 4+ years of experience with min 2–4 years of professional experience in software/AI development, with exposure to Generative AI and agentic AI.
- Experience contributing to AI/GenAI or software projects in a collaborative team setting.
- Exposure to cloud-based AI/ML environments (AWS, GCP, or Azure) is a plus.
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Job Family Group: Technology------------------------------------------------------
Job Family:Applications Development------------------------------------------------------
Time Type:Full time------------------------------------------------------
Most Relevant Skills Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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.
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View Citi’s EEO Policy Statement and the Know Your Rights poster.
Skills Required
- Proficiency in Python for GenAI development, data preprocessing, and scripting
- Solid understanding of core generative AI concepts (foundation models, LLMs, tokenization, embeddings, context windows)
- Hands-on experience with prompt engineering (zero-shot, few-shot, chain-of-thought, role-based prompting)
- Practical experience building RAG systems, including chunking, embeddings, vector databases, and semantic search
- Familiarity with knowledge graphs and interest/experience in Graph RAG
- Exposure to agentic AI development (building tool-using agents or multi-step workflows) using frameworks like Google ADK, LangGraph, CrewAI, or OpenAI Agents SDK
- Awareness of agent tooling and protocols, including tool/function calling, the Model Context Protocol (MCP), and familiarity with A2A protocol
- Experience consuming major GenAI APIs (OpenAI, Gemini, Claude) and exposure to orchestration frameworks such as LangChain or LlamaIndex
- Understanding of application deployment and containerization (Docker)
- Working knowledge of version control systems (Git)
- Awareness of AI compliance, data privacy, guardrails, and responsible AI principles
- Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field
- 4+ years of experience with minimum 2-4 years of professional software/AI development exposure
- Experience contributing to AI/GenAI or software projects in a collaborative team setting
- Exposure to cloud-based AI/ML environments (AWS, GCP, or Azure)
Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
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