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Tier-1 brand, metro location, mid-level generalist title, and broad LLM skill demand.
Core ML/LLM skills are transferable, but banking domain, credit/AML context and compliance increase specificity.
Explicit 1–3 year requirement and mandatory PySpark/Python, SQL, and LLM/Deep Learning skills.
Own end-to-end development and deployment of deep learning and generative AI solutions for North America Consumer Bank business problems.
Design, fine-tune, and implement large language models (LLMs) and generative AI applications using prompt engineering, RAG, and model fine-tuning techniques.
Ensure model validation, governance, compliance with Citi standards, and collaborate across teams to integrate AI-driven insights into business decisions.
1-3 years of hands-on experience in AI, machine learning, or data science with focus on deep learning and generative AI.
Proficiency in Python, PySpark, SQL, and experience with frameworks like TensorFlow, PyTorch, Hugging Face Transformers, LangChain, LlamaIndex.
Experience working with transformers/LLMs (e.g., OpenAI, Claude, Gemini), prompt engineering, RAG architectures, and managing large datasets.
Bachelor's or master’s degree in Economics, Statistics, Mathematics, IT, Computer Applications, Engineering, or related field from a premier institute.
Strong technical expertise in deep learning, LLMs, and generative AI applied to financial or retail banking domains, preferably credit cards and retail banking.
Ability to translate complex business problems into modeling frameworks and deliver actionable analytic solutions aligned with client needs.
Experienced in handling multi-stakeholder environments with strong communication, risk awareness, and control orientation.