Lead Software Engineer — Enterprise Applications

Role Overview

Citi's Banking Technology organization is seeking a highly hands-on Lead Software Engineer to design, build, and deploy cutting-edge AI solutions for Investment, Corporate, and Commercial Banking.

This role focuses on implementing scalable, agentic AI frameworks and generative AI solutions within small, agile squads. The successful candidate will operate with an AI-first mindset , emphasizing rapid prototyping, MVP-driven development, and iterative delivery of production-grade AI capabilities.

Key Responsibilities

• AI Solution Development: Design, implement, and deploy scalable agentic AI frameworks and generative AI solutions for critical business use cases, ensuring robustness, performance, security, and reliability.
• Agentic Systems: Develop and integrate agentic AI systems leveraging multiple model providers and platforms (e.g., OpenAI, Anthropic, Google APIs).
• Full-Stack AI Engineering: Build full-stack applications integrating LLM-driven workflows and AI coding tools (e.g., Devin, GitHub Copilot).
• Iterative Delivery: Drive an MVP-first, rapid-iteration approach with continuous experimentation and improvement.
• Evaluation & Optimization: Define and implement metrics, evaluation strategies, and feedback loops to continuously improve AI system performance and behavior.
• Emerging Technologies: Research, prototype, and integrate advances in agent-based, autonomous, and generative AI technologies.
• Technical Collaboration: Contribute hands-on within cross-functional teams and provide technical guidance and mentorship to junior engineers.
• Domain Alignment: Ensure AI solutions are well-aligned to banking and financial services requirements, constraints, and business outcomes.

Qualifications

• Programming: Strong proficiency in Python and/or Java (Spring Boot); working knowledge of JavaScript/TypeScript (Angular, Node.js). Full-stack experience is a plus.

AI/ML Expertise

• Solid understanding of core AI concepts such as knowledge representation, planning, and multi-agent systems.
• Hands-on experience with LLMs, RAG, prompt engineering, MCPs, and agent frameworks (e.g., Google ADK).
• Practical experience with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas).
• Familiarity with AI coding tools such as Devin, Claude Code, GitHub Copilot, and Antigravity.
• Software Engineering Strong grounding in modern engineering practices including Git, CI/CD, testing, code reviews, agile delivery, application resiliency, and security.
• Architecture Experience designing API-first, microservices-based and event-driven architectures, including data engineering patterns for AI systems.
• Platforms Hands-on experience with Docker, Kubernetes, and OpenShift.
• Problem Solving & Communication Strong analytical skills with the ability to clearly communicate complex technical concepts.
• Domain Knowledge Solid understanding of