Join Citi’s Startups & Partnerships team to design, test, and ship production AI applications that address real business needs across Citi.

Architect, build, and deploy production-grade AI-powered tools, pipelines, and workflows across Citi’s lines of business
Run structured proof-of-concepts and sandbox bake-offs to evaluate emerging AI technologies for real-world fit, including LLMs, agentic frameworks, and orchestration layers
Convert business ideas into working demos in days, then iterate rapidly using stakeholder feedback
Integrate LLMs and agentic systems with enterprise APIs, data pipelines, and lightweight front-end or internal tooling layers to deliver end-to-end solutions
Partner with governance, risk, and platform teams to move validated prototypes toward scaled deployment
Communicate technical progress through clear, compelling narratives for senior business and technology stakeholders

Full-stack engineer with applied AI/LLM experience; not a data science or ML research role
Proven experience building and shipping AI applications, pipelines, and workflows in live, production environments
Hands-on proficiency with Large Language Models (LLMs), prompt engineering, and agentic frameworks and orchestration tools (e.g., LangChain, LlamaIndex, AutoGen, or similar)
Strong software engineering fundamentals across the stack, including backend services, API integrations, and lightweight front-end or internal tooling layers
Strong communication and stakeholder management skills, working directly with business leaders and clients
Track record of quickly learning and adopting new tools and platforms as the AI landscape evolves
Comfort operating in a large, regulated, global institution while working with a startup-speed mindset

Medical, dental & vision coverage
401(k)
Life, accident, and disability insurance
Wellness programs
Planned time off (vacation)
Unplanned time off (sick leave)
Paid holidays
Discretionary and formulaic incentive and retention awards (for eligible employees)

Experience working directly with startups, venture teams, or technology scouting functions
Familiarity with vector databases, retrieval-augmented generation (RAG), or knowledge graph-based retrieval
Experience with containerization and cloud-native deployment (Docker, Kubernetes) for AI services
Prior exposure to financial services, banking, or other highly regulated industries
Experience presenting technical concepts to non-technical, senior business audiences

AI Engineer – Startups & Partnerships

Job Description