AI Applied Senior Engineering Lead

Citi’s Banking Technology organization is seeking a highly hands-on AI Applied Senior Engineering Lead 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, Git Hub 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/Type Script (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 (Tensor Flow, PyTorch, Scikit‑learn, Num Py, Pandas).
Familiarity with AI coding tools such as Devin, Claude Code, Git Hub 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 Open Shift.
Problem Solving & Communication:
Strong analytical skills with the ability to clearly communicate complex technical concepts.
Domain Knowledge:
Solid understanding of banking/financial services and regulated environments.
Nice to Have:
Experience in data science (statistical modeling, experimentation) and interest in mentoring junior AI engineers.
Experience   10+ years of progressive software engineering experience with a strong hands‑on coding background.
2+ years focused on AI software development, LLM‑based solutions, agentic systems, and/or machine learning.
Demonstrated experience delivering enterprise‑scale AI capabilities into production.
Education  Bachelor’s or Master’s degree in Computer Science, AI, Robotics, or a related quantitative discipline.
This job description provides a high‑level review of the types of work performed. Other job‑related duties may be assigned as required.
Job Family Group:
Technology
Job Family:
Applications Development
Time Type:
Full time
Primary Location Full Time Salary Range: $ - $
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