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Tier-1 brand, mid-level experience band, and metro location increase applicant competition density.
Core ML and LLM skills are transferable across industries, though banking domain knowledge is beneficial.
Explicit 4-6 years plus mandatory LLM/RAG and ML skillset creates high filtering rigor.
Design, develop, and deploy end-to-end AI solutions focusing on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
Lead advanced prompt engineering and testing frameworks to ensure performance, accuracy, and safety of LLM-based applications.
Use machine learning techniques like Gradient Boosting, XGBoost, and clustering to generate insights and solve complex business problems.
4-6 years of hands-on experience in AI, ML, or data science roles.
Strong foundation in classical ML algorithms such as Gradient Boosting, Clustering, and XGBoost.
Proven experience building end-to-end RAG pipelines and advanced prompt engineering with LLMs.
Bachelor's degree in Computer Science, Statistics, Engineering, Mathematics, or equivalent quantitative field.
Deep expertise in modern AI landscape, especially LLMs, with ability to apply tech to real-world business challenges.
Strong proficiency in Python and SQL for handling large complex datasets and AI/ML workflows.
Capable of translating complex AI/ML insights to technical and non-technical stakeholders and working in matrix teams.