Dynamic, deadline-driven, and results-oriented Data Science professional with 9 years of IT experience, including over 6 years specializing in data science. Adept at designing, developing, and deploying high-impact, data-driven solutions across various industries, with a strong focus on the banking sector, particularly in IFRS 9 models like Probability of Default, Cyclical Factor, and Loss Given Default etc.• Expertise in advanced machine learning, deep learning, and cloud deployment technologies, including Docker and Kubernetes.• Proven record of optimizing models, creating robust data pipelines, and ensuring efficient data processing.• Highly skilled in translating complex data insights into actionable business strategies, contributing to significant performance improvements and decision-making processes.Technical Skills ---Programming Languages: Python, R, ORE, YAML, YML, Shell Scripting, SQLMachine Learning (ML): Generalized Linear Model (GLM), Logistic Regression, Classification, Random Forest, Decision Trees, SVM, k-means, Clustering, Time-Series AnalysisDeep Learning (DL): Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN - LSTM, GRU), Encoders-Decoders, Attention Mechanisms, TransformersGenerative AI models: GPT, LlaMa, MistralGEN AI Technology: Large Language Models (LLM), LangChain, Vector Databases, Retrieval-Augmented Generation (RAG), Prompt EngineeringCloud Platforms: Oracle Cloud Infrastructure (OCI), Technologies/Frameworks: SaaS, Docker, Kubernetes, CI/CD Pipelines, MLOps, Git, GitLab, NLP, TensorFlow, Scipy, Model Management and Governance, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Hypothesis Testing. Professional Experience ---Project: GENAI (Large Language Model)• Utilized Large Language Model (LLM) technology to generate high-quality, synthetic data for the Banking domain.• Designed and implemented a robust data generation pipeline incorporating Mistral and Llama models, resulting in a 150% increase in data volume and significantly improving data availability for analysis and model training.POC Project: • Generated Management Reports: After building and validating the ML model, utilized Large Language Models (LLMs) to generate detailed, comprehensible textual reports, providing valuable insights and actionable recommendations to management.• RAG-based Conversational AI: Designed and developed a Retrieval Augmented Generation (RAG) chatbot to optimize user experience by providing quick and accurate access to product installation and user guides.