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Tier-1 brand, broad ML/AI skillset requirements, and metro hybrid role increase applicant competition.
Specialized agentic AI expertise makes cross-industry transfer moderate; banking domain experience is a preferred differentiator.
Explicit 10+ years plus multiple mandatory ML/AI technical requirements and production deployment skills make filters highly strict.
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Lead design, implementation, and deployment of scalable agentic AI frameworks and generative AI solutions for banking applications with focus on performance, reliability, and security.
Build full-stack applications integrating ML/LLM tools, driving rapid MVP iterations, and conducting A/B experiments for continuous AI solution improvement.
Provide hands-on technical leadership and collaborate with AI researchers, data scientists, product managers, and engineers to scale AI across Citi’s products and services.
10+ years of progressive software engineering experience with hands-on coding and successful AI production deployments.
Minimum 3+ years dedicated experience in AI, machine learning, generative and/or agentic AI systems development.
Bachelor's degree in Computer Science, IT, AI, Robotics or related quantitative field; Master’s degree preferred.
Expert-level Python proficiency (FastAPI, Django, Flask, PySpark), experience with ML frameworks (TensorFlow, PyTorch), AI frameworks (LangChain, AutoGen), and strong understanding of AI concepts including multi-agent systems and decision-making under uncertainty.
Technical leader with proven ability to architect and deliver enterprise-scale AI projects using agentic and generative AI technologies in regulated banking environments.
Experienced full-stack developer with deep knowledge of AI service APIs, microservices, event-driven architectures, CI/CD, and container technologies (Docker, Kubernetes).
Hands-on problem solver who drives rapid iterative development and experimentation, capable of bridging AI research and scalable production engineering.