Designed and implemented data warehousing solutions to store and manage large volumes of structured and unstructured data efficiently.Built orchestration pipelines using Apache Airflow to automate data workflows, ensuring data consistency and reliability across systems.Configured Apache Airflow DAGs (Directed Acyclic Graphs) to schedule and monitor data pipeline tasks, including data extraction, transformation, and loading (ETL).Integrated MS SQL Server as the primary database backend for storing and querying structured data, ensuring data integrity and security. Implemented ETL processes within Apache Airflow workflows to extract data from various sources, transform it using SQL transformations, and load it into MS SQL Server databases.Utilized Cloud storage with Amazon S3 as a cost-effective solution for storing and managing large datasets, ensuring scalability and durability. Incorporated Apache Airflow operators for interacting with S3 buckets, including uploading, and downloading data files, as well as performing data processing tasks directly on S3 objects.Designed and implemented dimensional modeling techniques within the data warehouse to optimize query performance and facilitate efficient data analysis. Collaborated with data scientists and analysts to define data pipeline requirements and optimize workflows for machine learning model training and inference.Implemented data governance and security measures within Apache Airflow, MS SQL Server, and S3 to protect sensitive data and ensure compliance with regulatory standards.Conducted performance tuning and optimization of Apache Airflow DAGs, MS SQL Server databases, and S3 storage configurations to improve pipeline efficiency and reduce latency.Worked closely with stakeholders to gather requirements, define data architecture, and implement solutions that meet business objectives.

Designed and implemented machine learning and deep learning models for image recognition, natural language processing, object detection, classification, and image segmentation to tackle real world problems.Experienced in a wide range of machine learning models, including Regression, SVM, and deep learning architectures like Yolo, Faster RCNN, Mask2Former, LSTM and GAN.Deployed AI models on AWS Elastic Bean and Kubernetes.Experienced with AWS S3 Storage.Managed and updated Azure DevOps repositories for machine learning complete pipelines and AI models to ensure version control and seamless collaboration within the team.Utilized Python scripting and libraries to solve real-time problems and automate existing code for improved runtime.Conducted data analysis and performed feature engineering to improve model accuracy and efficiency.Collaborated with cross-functional teams to integrate machine learning models into production systems, ensuring high performance and reliability.Created interactive plots to visualize and communicate data analysis results effectively.Developed Power BI reports and dashboards to provide actionable insights to clients.Contributed to database schema design and maintained and updated databases using MySQL.Involved in spatial data creation, quality control, and Spatial database creation via Python scripting and ArcGIS Catalog.Utilized lidar point cloud data to calculate asset distance from the camera and object height.Designed complete pipelines for project delivery, including geolocation-based image extraction, model inference, photogrammetry, and GeoDatabase creation.Utilized Python scripts for MySQL database updates and visualization of geospatial data on maps.Worked as a Team Lead, managing projects, conducting technical interviews, and onboarding new team members.Managed and supervised the AI-team, including client meetings, project understanding, and meeting project timelines and budgets.

Developed and deployed machine learning models and systems, utilizing advanced algorithms to address business challenges in areas like image classification and object detection.Employed data analysis techniques and applied feature engineering methods to enhance the accuracy and efficiency of the machine learning models.Collaborated with diverse teams to seamlessly integrate machine learning models into production systems, ensuring optimal performance and reliability.Conducted in-depth analysis of extracted data, presenting the findings through interactive plots forclear visualization.