• Orchestrated end-to-end data science projects, including data collection, extraction, preprocessing, and preparation for AI modeling, ensuring high-quality and reliable data inputs.• Finetuning modern LLM’s (Falcon, Llama) for specific downstream NLP tasks using instruction-based tuning. Optimizing model for deployment using PEFT (Parameter Efficient Tuning) methods. Utilized prompt engineering techniques using LangChain to finetune model outputs for specific tasks.• Leveraging AWS to develop, build, test, and deploy scalable machine learning pipelines, enabling rapid model iteration and deployment. • Collaborated closely with cross-functional teams to define project requirements, gather domain insights, and translate business problems into machine learning solutions.• Communicated complex technical concepts and findings to stakeholders and non-technical teams in a clear and concise manner, fostering effective collaboration and decision-making.

• Conducted comprehensive data collection for 60 distinct weight lifting exercises by leveraging web scraping techniques in Python.• Utilized OpenCV and MediaPipe libraries to perform precise pose detection, enabling the identification and extraction of X, Y, and Z coordinates of body joints.• Employed advanced feature engineering techniques to transform the collected coordinate data, creating additional informative features that enhanced the overall analysis and modeling process.• Leveraged machine learning algorithms such as KNN, Decision Tree, Random Forest, and XGBoost to differentiate weightlifting exercises based on the generated data, achieving an impressive accuracy of up to 75%. • Contributed to the development of a robust machine learning pipeline for exercise classification, enabling accurate exercise recognition and potential applications in performance tracking and personalized training recommendations.

• Spearheaded cross-functional collaboration and provided leadership to five teams, consisting of four field service engineers and 12 technicians, ensuring seamless project evaluation, proposal, and implementation processes.• Led the analysis of turbine wind speed and production data, leveraging data-driven insights to identify root causes behind lower turbine production efficiency. Through the implementation of targeted improvements, production estimates were significantly refined, resulting in a 30% reduction in reading errors and delivering more accurate and reliable information to customers.• Led customer meetings, addressing queries, providing solutions, and presenting turbine productivity reports, while facilitating discussions on efficient resolutions for identified issues.