Lead AI Engineer

Role Overview

We are seeking an AI‑first Lead AI Engineer to define, architect, and deliver enterprise‑scale AI solutions across Investment, Corporate, and Commercial Banking.

This role combines deep hands‑on technical leadership with architectural ownership, driving the adoption of LLMs, agentic workflows, and generative AI platforms to improve efficiency, automation, simplification, and risk reduction. The Lead AI Engineer will shape both technical delivery and engineering culture, partnering closely with multiple technology and business teams.

Responsibilities

• Lead integration of functions to meet goals, deploy new products, and enhance processes
• Analyze complex business processes, system processes, and industry standards to define and develop solutions to high level problems
• Provide expertise in area of advanced knowledge of applications programming and plan assignments involving large budgets, cross functional project, or multiple projects
• Develop application methodologies and standards for program analysis, design, coding, testing, debugging, and implementation
• Utilize advanced knowledge of supported main system flows and comprehensive knowledge of multiple areas to achieve technology goals
• Consult with end users to identify system function specifications and incorporate into overall system design
• Allocate work, and act as an advisor/coach developers, analysts, and new team members
• Influence and negotiate with senior leaders and communicate with external parties
• Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
• Lead the design, hands‑on implementation, and deployment of scalable agentic AI and generative AI solutions for high‑impact business use cases.
• Architect advanced agent‑based systems (perception, reasoning, planning, execution), integrating multiple LLM providers (OpenAI, Anthropic, Google).
• Build and deliver production‑grade, full‑stack AI applications using modern ML/LLM platforms and AI coding tools (, Devin, GitHub Copilot, Antigravity).
• Champion MVP‑driven delivery, rapid iteration, hypothesis testing, and experimentation (A/B testing) to achieve fast time‑to‑value.
• Define rigorous evaluation frameworks and metrics for AI and agent performance, ensuring continuous optimization and alignment with business goals.
• Proactively evaluate and integrate emerging AI technologies, particularly in agent‑based and autonomous AI systems, while acting as a thought leader and coach.
• Provide front‑line technical leadership, guide architectural decisions, and mentor engineers across AI, data, and application teams to build an AI‑first engineering culture.
• Ensure all AI solutions are grounded in deep banking domain understanding and clearly articulate business and risk impact beyond pure technical implementation.

Qualifications

• 10+ years of progressive software engineering experience with sustained hands‑on contributions.
• 3+ years of dedicated experience in AI, LLM‑based systems, and agentic architectures.
• Proven success architecting and delivering multiple enterprise‑scale AI solutions into production.
• Expert‑level proficiency in Python and/or Java (Spring Boot); solid experience with JavaScript/TypeScript (Angular,
• Deep knowledge of AI fundamentals (knowledge representation, automated planning, decision‑making under uncertainty, multi‑agent systems).
• Extensive hands‑on experience with LLM platforms (OpenAI, Anthropic, Google), RAG pipelines, MCPs, and agent frameworks (Google ADK).
• Demonstrated effectiveness with AI coding tools (Devin, Claude Code, GitHub Copilot, Antigravity).
• Strong experience with ML frameworks (TensorFlow, PyTorch) and data science libraries.
• Proven ability to design and expose robust, scalable APIs for AI services.
• Deep expertise in software engineering best practices, including system design, CI/CD, testing strategies,