Lead Software Engineer — Enterprise Applications

Citigroup; Mississauga; On-site; CAD 120,000 - 160,000; Full time

Job summary

Citigroup is seeking a hands-on Lead Software Engineer to design, build, and deploy scalable AI solutions for Investment, Corporate, and Commercial Banking. The role emphasizes an AI-first mindset with rapid prototyping and MVP-driven delivery in agile squads.

You will lead full-stack AI engineering, integrate LLM workflows, and mentor junior engineers while ensuring alignment with banking domain requirements.

Qualifications

• Strong Python/Java development experience for AI-powered apps.
• Experience with LLMs, RAG, prompt engineering.
• Familiar with TensorFlow, PyTorch, Scikit-learn.
• Knowledge of API-first, microservices architectures.

Responsibilities

• Design, build, and deploy scalable AI solutions for banking use cases.
• Develop agentic AI systems with multiple model providers (OpenAI, Anthropic, Google).
• Create full-stack AI apps integrating LLM-driven workflows.
• Drive MVP-first, rapid-iteration delivery with experimentation.
• Define metrics and feedback loops to improve AI performance.
• Research and prototype advances in autonomous generative AI.
• Collaborate with cross-functional teams and mentor junior engineers.
• Align AI solutions with banking and financial services needs.

Skills

Python

Java (Spring Boot)

JavaScript/TypeScript

Full-stack experience

Tools

Docker

Kubernetes

OpenShift

Git

CI/CD

Job description

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