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Tier-1 brand and metro location increase competition, but seniority and niche GenAI specialization reduce applicant density.
Highly domain-specific ML/GenAI production expertise required, limiting cross-industry transferability.
Mandates 12+ years, deep GenAI/LLM/MLOps and production experience, so hiring filters will be stringent.
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Lead development and deployment of scalable, production-ready Generative AI (GenAI) and agentic AI frameworks for critical banking applications.
Architect and build full-stack AI solutions integrating Machine Learning (ML), Large Language Models (LLM), and GenAI technologies into Citi’s systems.
Drive AI innovation through prototyping, setting technical standards, and ensuring secure, cloud-native, containerized production deployments.
12+ years of hands-on experience in engineering and executing scalable enterprise AI/technology solutions.
Bachelor's degree in Computer Science, Computer Engineering, or related technical field.
Expert proficiency in Python programming and proven experience with Generative AI frameworks like LangChain, AutoGen, or CrewAI.
Advanced knowledge and hands-on experience with Large Language Models (LLM), vector databases, Retrieval-Augmented Generation (RAG), MLOps tools (e.g., MLflow), and containerization tools (e.g., Docker).
Senior technologist with deep expertise specifically in agentic AI, Generative AI, and full-stack AI application development within enterprise or banking environments.
Experienced in building and scaling production AI services using cloud-native and container-first architectures.
Capable of collaborating cross-functionally with AI researchers, data scientists, and product managers while driving technical design, prototyping, and deployment at scale.