AI Engineer – Startups & Partnerships

Overview

In this role you build and deploy production-grade AI applications that solve real business problems across Citi’s lines of business. You work closely with the Startups & Partnerships team to evaluate new AI tech and bring fast, real-world value to users at scale. You will prototype quickly, integrate LLMs and agentic systems with enterprise data, and shepherd solutions from concept to production with governance in mind. This is a hands-on, startup-like role inside a global bank, offering high-impact work and deep access to business requirements. You’ll help shape Citi’s AI strategy by delivering tangible AI-enabled capabilities that matter to the business and customers.

Pay / Benefits

• 27 days annual leave (plus bank holidays)
• discretional annual performance bonus
• Private Medical Care & Life Insurance
• Employee Assistance Program
• Pension Plan
• Paid Parental Leave

Responsibilities

• Architect, develop, and deploy production AI-powered tools and pipelines across Citi’s lines of business
• Conduct structured PoCs and sandbox evaluations of new models and platforms
• Turn business ideas into working demos rapidly and iterate with stakeholder feedback
• Integrate LLMs and agentic systems with enterprise APIs and data pipelines for end-to-end solutions
• Move validated solutions from prototype to scaled production in collaboration with governance and platform teams
• Translate technical work into clear narratives for senior business and technology stakeholders

Key requirements

• Full-stack engineer with applied AI/LLM experience; not a pure data science role
• Proven experience shipping AI applications and workflows in production
• Hands-on proficiency with LLMs, prompt engineering, and agentic frameworks/orchestration tools (e.g., LangChain, LlamaIndex, AutoGen)
• Strong software engineering fundamentals across backend, APIs, and lightweight frontend or tooling
• Strong communication and stakeholder management to work with business leaders
• Demonstrated ability to learn new tools quickly as AI landscape evolves
• Comfort operating in a large, regulated institution with startup-speed mentality
• Experience working with startups, venture teams, or technology scouting functions
• Desirable: familiarity with vector databases, RAG, or knowledge graph retrieval
• Desirable: containerization and cloud-native deployment (Docker, Kubernetes) for AI services
• Desirable: experience in financial services or other regulated industries
• Desirable: ability to present technical concepts to non-technical senior audiences

• strong communication
• stakeholder management
• adaptability
• LLMs
• prompt engineering
• agentic frameworks

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