Back
Tier-1 brand and metro location increase competition, but seniority and niche GenAI skills reduce candidate density.
Specialized GenAI, LLM, and financial XVA domain expectations make industry experience and domain fit critical.
Explicit 12+ years plus required GenAI, LLM, MLOps and production experience enforce stringent shortlisting filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead development and deployment of scalable, secure agentic AI and Generative AI solutions for banking risk management use cases.
Architect and implement full-stack AI applications integrating Machine Learning and Large Language Models into Citi’s trading and risk systems.
Drive AI prototyping, performance evaluation, and transition of AI capabilities from proof-of-concept to enterprise production deployments.
12+ years of hands-on experience in engineering scalable enterprise AI solutions.
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field.
Expert proficiency in Python programming language mandatory.
Proven experience with Generative AI frameworks (LangChain, AutoGen, CrewAI) and Large Language Models including vector databases and RAG implementation.
Experienced in architecting and deploying AI frameworks and production-ready GenAI applications in highly regulated financial environments.
Strong expertise in AI agentic systems, ML, NLP, GenAIOps, MLOps, and containerization (Docker).
Skilled in collaborating with multidisciplinary teams (AI researchers, data scientists, product managers, engineers) to scale AI across enterprise risk products.