• Created a real-time license plate recognition system using PyTorch, CUDA, and TensorRT, deployed on AWS ECS with CI/CD via GitLab, achieving 30 FPS and increasing vehicle throughput by 25%.• Created a data warehousing solution on Snowflake with Python for processing large volumes of transactional data, utilizing SQLAlchemy for seamless data integration, which enhanced query performance by 50% and streamlined data access for analytics teams.• Developed a time series forecasting model using PyTorch Lightning and Prophet, managed with DVC for data versioning and Azure Data Factory, increasing forecast accuracy by 8% and reducing inventory costs by 12%. Implemented a LLM-based medical report generator using PyTorch and LLaMA, deployed via AWS Lambda with CI/CD through CircleCI, increasing reporting efficiency by 30%.• Built a customer segmentation model using Python and Snowflake, applying clustering algorithms to identify target demographics, resulting in a 25% increase in marketing campaign effectiveness and a 30% reduction in customer acquisition costs.• Developed a recommendation engine using TensorFlow and PySpark, integrated with Snowflake and monitored with MLFlow, improving CTR by 22% and boosting revenue by 15%.• Implemented a route optimization engine using FastAPI, PyTorch, and Airflow, with model updates automated using Kubeflow pipelines, cutting delivery times by 15% and fuel costs by 12%.• Developed a customized deep learning model to detect track vehicles in video feeds from cameras mounted on trucks, using Tensorflow and Python integrating AWS API Gateway for the secure handling of RESTful APIs, facilitating flawless data exchange between the front-end interface and serverless backend architecture.• Developed a real-time vehicle license plate recognition system with PyTorch, CUDA, and TensorRT, deployed on AWS ECS, utilizing GitLab CI/CD, achieving 30 FPS and increasing vehicle throughput by 35%.

• Designed and deployed a deep learning-based defect detection system using CNNs with OpenCV and TensorFlow, containerized in Docker, and deployed on AWS SageMaker, achieving 95% accuracy and cutting inspection time by 70%.• Developed a conversational AI chatbot using Hugging Face and DialogFlow, integrated with AWS SageMaker and CircleCI pipelines, improving customer satisfaction by 20% and decreasing call center volume by 50%.• Implemented a sales forecasting solution using Python and Snowflake, leveraging ML models to predict sales trends with 94% accuracy, aiding inventory management and strategic decisions.• Engineered a real-time object detection pipeline using YOLOv8, CUDA, Kafka, and MLFlow, deployed on Kubernetes, achieving 95% detection accuracy with reduced latency to 50ms.• Created a real-time data analytics dashboard with Snowflake and Python, utilizing Apache Airflow for ETL orchestration, enabling faster report generation and cutting report creation time by 70%.• Built a fraud detection model with LSTMs and PyTorch, deployed on AWS Lambda using Kafka and MLOps pipelines, achieving 96% accuracy and reducing financial losses by 35%.• Designed a medical image segmentation model using TensorFlow and OpenCV, deployed on AWS SageMaker with CI/CD pipelines through GitHub Actions, achieving 95% accuracy and accelerating diagnosis by 25%.• Developed an end-to-end MLOps pipeline with Kubeflow, DVC, Docker, and GitHub Actions, deployed on AWS Lambda, reducing machine downtime by 30% through predictive failure detection.• Customized a CCTV surveillance system framework for real-time object detection and tracking based on client specifications, achieving 95% accuracy.

• Created a document classification model using Hugging Face and PyTorch, integrated with MLFlow for tracking, and deployed on GCP, cutting manual categorization efforts by 40% while achieving 92% accuracy.• Built a crop health monitoring system using TensorFlow and PySpark, deployed via Vertex AI pipelines, enhancing yield predictions by 15% and reducing crop loss by 20%.• Developed a real-time sentiment analysis engine using TensorFlow, FastAPI, and Kubernetes, with CI/CD via GitLab on Azure and AWS ECS, achieving 98% accuracy and improving API response times by 40%.• Designed and implemented interactive user interfaces and data visualization components with Vue.js and Vuex, offering clients seamless access to their cloud billing data.• Developed a customized neural network-based anomaly detection system, reducing false positives by 83%, integrated with backend APIs and a web framework for seamless performance.