Junior Generative AI Application Developer

Engineer the future of global finance. At Citi, our Tech team doesn't just support finance—we are helping to redefine it. Every day, $5 trillion crosses through our network. We do business in 180+ countries operating at a scale few can match. From deploying advanced AI to helping shape global markets, we build systems that matter. Look to join a team where your work helps influence economies, your ideas can drive innovation and outcomes, and your growth is backed by mentorship, continuous learning, and flexibility with potential hybrid work opportunities.

Help solve real-world challenges that touch millions and get the opportunity to build the future of finance with Citi Tech.

We provide you with the knowledge and skills you need to succeed... We’re committed to teaching you the ropes. For individuals starting their careers, we offer a supportive environment. Here at Citi, internal development paths are intended to help you build a broad skillset and accelerate your career growth by gaining exposure to more than one team in Software Development. Our supportive environment will help you discover the best fit for your skills and long-term career goals at Citi.

Your time here will look like this... You will thrive in an agile software development environment, crafting high-quality, scalable software solutions leveraging cutting-edge technologies, including AI-powered coding tools. Translating business requirements into robust code, you’ll ensure adherence to stringent quality standards and provide essential support through testing cycles and post-production deployment.

To deliver systems at the enterprise-level that are high quality, scalable, and resilient, you will skillfully utilize advanced tools for code testing and debugging, while learning from top technologists through design sessions and peer code reviews. Furthermore, you’ll benefit from a comprehensive support system: you’ll join a cohort of new hires, embarking on a shared journey to master our company culture, tools, processes, and the technical proficiencies vital to your success.

While technologies may vary across teams, and AI tooling is rapidly evolving, developers will gain exposure to a wide range of technologies and tools across all aspects of the SDLC:

• Back-End Microservices Development:
  Java, Spring Framework
• Mobile Development (iOS):
  Swift, Xcode, MVC Architecture
• Mobile Development (Android):
  Android Studio, JavaScript, CSS, AJAX, Java Web Services
• Front-End Web Development:
  React or Angular, Apigee Type Script, HTML5
• Generative AI & AI Agents:
  Prompt Engineering, Workflow Design, and GenAI Optimization
• Tools & Platforms:
  Git Hub Copilot (multiple models), Citi Squad (automated code reviews), Devin.
  
  AI (agentic code generation), Stylus Work spaces (in‑house Gemini)
• Large Language Models (LLMs):
  Gemini, OpenAI, Claude, Llama, Local Models
• Frameworks:
  Lang Chain, Llama Index, Hugging Face
• Orchestration:
  Lang Graph, Multi-Agent Systems
• Development
  
  Languages:
  
  Java, PythonFastAPI
• Retrieval-Augmented Generation (RAG):
  Postgre
  
  SQL, Vector DBs, Advanced Retrieval
• ML/DL Frameworks:
  PyTorch, Tensor Flow, Fine-tuning
• Deployment:
  Enterprise Dev Ops Pipeline, Docker, Monitoring Tooling
• Data Analytics:
  Utilizing tools like Splunk, Tableau, and Adobe Analytics to track customer journey adoption and measure the business value of delivered products.

• Gen AI Strategy & Cloud Deployment:
  Contributing to the adoption and deployment of cutting-edge Generative AI models and solutions on diverse cloud providers such as AWS, leveraging specialized AI/ML services for scalable and efficient inference. This includes advanced AI engineering practices to optimize model performance and reliability.
• Engineering Excellence (AI/MLOps):
  Adhering to and implementing Citi’s Minimum Development Standards and Minimum Operational Standards, with a strong focus on applying MLOps best practices, ethical AI guidelines, and responsible AI development principles, particularly for managing the lifecycle of Generative AI agents.
• Architectural Patterns (Gen AI Focus):
  Exploring and applying the latest design and software architecture patterns and…