Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.
We are building the most consequential AI solutions in Funds Transfer Pricing and Financial Hedging platforms, and we are seeking a Lead AI Engineer to own the technical vision and drive the execution for these domains.
In this position, you will be accountable for the end-to-end technical success of our agentic solutions, from architectural vision and design through implementation and adoption, ensuring they deliver scalable, resilient, and business-impacting capabilities at global scale.
Responsibilities:
Strategy & Technical Ownership
- Define and own the end-to-end technical strategy for agentic AI within the Funds Transfer Pricing and Financial Hedging domains, ensuring alignment with both business objectives and the firm's technology standards.
- Serve as the ultimate technical authority on agentic systems, providing expert guidance to senior leadership and translating complex business challenges into a clear, actionable technical roadmap.
- Lead by example through hands-on coding, personally architecting and contributing to the most critical and complex components of the system, setting the standard for code quality and innovation.
- Mentor and cultivate a team of senior AI engineers, fostering a culture of technical excellence, continuous learning, and collaborative problem-solving.
System Architecture & Design
- Own the architectural vision for highly scalable, resilient, and performant multi-agent systems, establishing the blueprint and design patterns that will be used across the platform.
- Oversee the design of intelligent agentic systems, including reasoning, planning, memory, orchestration, and action execution, ensuring they are scalable, reliable, auditable, and fit for business-critical workflows
Development Guidance & Implementation
- Guide the team in implementing robust AI agents while personally driving the development of core components and complex features.
- Set the strategy for integrating advanced technologies, including large language models (LLMs), predictive models, and sophisticated reasoning frameworks, to continuously expand agent capabilities.
- Establish and enforce best practices for designing and optimizing Retrieval-Augmented Generation (RAG) architectures and vector data strategies.
Quality, Performance & Governance
- Own the comprehensive strategy for AI system quality, defining the frameworks and metrics for measuring and optimizing agent performance, reliability, and task success.
- Establish and enforce rigorous standards for AI governance, explainability (XAI), and responsible AI, ensuring systems are transparent, auditable, and compliant.
Required Qualifications & Skills
- Extensive professional experience in software development and system design, with at least 5 years in a technical leadership capacity, guiding senior engineering teams in delivering large-scale, complex systems.
- Architectural Mastery of the AI Ecosystem: A proven track record of architecting solutions with the modern AI ecosystem. This requires deep, hands-on expertise with frameworks for agent development (Google ADK), multi-agent orchestration (LangGraph, AutoGen, CrewAI), and data augmentation (LangChain, LlamaIndex).
- Proven Expertise in Agentic Systems: A track record of architecting and delivering complex single- and multi-agent systems, demonstrating expertise in planning/reasoning engines, memory systems, and agentic protocols like MCP.
- Deep, Practical Knowledge of LLMs: Demonstrated mastery of LLM fundamentals, Prompt Engineering, and Context Engineering, with a history of applying this knowledge to build sophisticated agentic architectures and design robust APIs for AI services.
- Deep expertise in enterprise Retrieval-Augmented Generation (RAG) architectures, including document ingestion, embedding strategies, retrieval optimization, reranking, vector databases, and context management.
- Expert-Level Engineering Craftsmanship: Expert-level proficiency in Python and SQL applied to building production-quality, high-performance AI systems.
- Proven ability to lead technical strategy, influence senior stakeholders, drive architecture decisions across multiple teams, and mentor senior engineers and technical leads.
Beneficial Qualifications & Skills
- Experience working in the financial services industry.
- Proficiency in Java as an additional programming language.
Education
- Bachelor’s degree/University degree in Computer Science
- Master's degree preferred
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Technology
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Applications Development
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Full time
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Please see the requirements listed above.
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For complementary skills, please see above and/or contact the recruiter.
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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.
