- Developed and implemented a recommendation system for Paramount Network and other cable channels to enhance content suggestions based on user preferences, viewing history, and trending shows, resulting in a 25% increase in viewer engagement. Used GPT-4 and SVD with k-fold cross-validation to ensure coverage, diversity and novelty of the recommendation system.- Designed and deployed a ranking system utilizing a BERT-based NLP model and TF-IDF with PyTorch to arrange content by analyzing and summarizing user comments, which improved content discoverability and user satisfaction by 30%.- Led the development of a survey analysis system using NLP technologies including AWS Bedrock, Lex and Copilot, along with clustering algorithms like K-means and DBSCAN, resulting in data-driven planning for films and television shows and improving project success rates by 20%.- Conducted A/B testing on content recommendation algorithms to ensure the balance of responsiveness and simplicity by analyzing user interaction data to refine model accuracy. Resulted 15% enhancement in viewer satisfaction metrics.- Implemented a real-time monitoring system for the recommendation engine using AWS CloudWatch and Apache Kafka, enhancing operational efficiency by 40% through analytics that track model performance and user engagement metrics.- Engineered a scalable cloud architecture on AWS - Lambda, EC2, SageMaker, S3, ensuring efficient data processing and model deployment, reducing costs by 20%.

- Led a cross-functional team of 30+ engineers and developers, fostering a culture of innovation and collaboration, resulting in the successful execution of AI/ML/Data projects for 16 clients.- Managed the development and launch of cloud-based platforms that streamlined data processing for over 10,000 users, utilizing Apache Spark and AWS Redshift, increasing operational efficiency by 35%.- Managed a technology budget exceeding $10 million, optimizing resource allocation and reducing costs by 20% through strategic vendor negotiations and process improvements.- Delivered strategic consulting for diverse clients, focusing on technology integration and process optimization, which increased client satisfaction ratings by 50%.- Integrated a feedback analysis and pain point prediction ML engine based on user footprints using scikit-learn and RAG, increasing customer engagement by 40%.- Created a scalable ML engine for the data pipeline using Apache Airflow, ensuring robust data governance practices to maintain data integrity and compliance, reducing data errors by 15%.

- Developed an NLP-based survey analysis tool to assess patient feedback and identify key areas for improvement, leading to a 30% enhancement in service delivery and overall patient satisfaction.- Created a monitoring system for patient progress using Python, Matpilotlib, NLTK, and LSTM, enhancing care management by 35%.- Built a rule-based chatbot using Rasa and Dialogflow to assist users in navigating services, increasing user satisfaction by 25%.- Executed testing for machine learning models using PyTest, establishing automated test cases that improved deployment reliability by 40%.- Established data quality assurance protocols, including automated validation checks with Great Expectations, resulting in a 20% reduction in data discrepancies.- Collaborated with cross-functional teams to define and implement data visualization strategies using Tableau and Power BI, enhancing reporting capabilities across departments and improving decision-making speed by 30%.