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Tier-1 brand, hybrid role, metro locations, and mid-level GenAI skillset drive high competition.
Requires specialized GenAI skills and financial-domain familiarity, so cross-industry transferability is moderate.
Explicit 1–3 years plus mandatory GenAI/LLM and toolstack requirements make screening stringent.
Design, train, and deploy Generative AI and LLM-based models powering a live client engagement platform for Citi's Wealth business, using Python, PySpark, and SQL.
Own analytics measuring platform performance and user adoption, translating insights into actionable product improvements.
Collaborate with product managers, engineers, and business stakeholders to move solutions from prototype to production, ensuring compliance and governance standards.
1–3 years of hands-on experience in AI, machine learning, or data science roles, including generative AI applications.
Proficiency in Python and SQL; experience with LLM frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, or LlamaIndex.
Bachelor’s degree in computer science, data science, statistics, mathematics, engineering, economics, or related quantitative field.
Work Experience Required: 1–3 years in relevant AI/ML/data science roles.
Experienced with full model lifecycle management from data extraction through production deployment and governance, especially in generative AI/LLM contexts.
Skilled in analyzing both structured and unstructured data to inform product decisions and strategic priorities within financial services, particularly wealth management.
Comfortable working in a cross-functional environment integrating AI models into live platforms with strong collaboration between data science, engineering, and product teams.