Data Engineer with experience in building data pipelines, serverless web applications, and CI/CD automation using AWS, Python, SQL, Docker, and GitHub Actions. Also experienced in machine learning engineering, data proce…
Data Engineer with experience in building data pipelines, serverless web applications, and CI/CD automation using AWS, Python, SQL, Docker, and GitHub Actions. Also experienced in machine learning engineering, data processing, and integrating LLM tools such as OpenAI API and RAG (Langchain).
<p>Developed a robust data pipeline for a job matching platform leveraging cloud tools and LLMs.<br>
Optimized user engagement and increased average session length by 13%.<br>
Authored a data-driven proposal detailing data pipeline design, cloud resources optimization, and cost analysts.<br>
Maintained high quality of work through a rigorous review process.<br>
Kept documentation up to date in Confluence.<br>
Built a serverless web application using AWS Chalice framework.<br>
Integrated AWS services such as S3, DynamoDB, and OpenSearch.<br>
Emphasized seamless data integration and real-time processing to enhance functionality and user experience.<br>
Implemented CI/CD pipeline with automated end-to-end testing and deployment using GitHub Actions, Pytest, and Docker.<br>
Achieved over 95% test coverage.<br>
Integrated Large Language Model tools such as OpenAI API and RAG (Langchain) for effective text processing in AI-driven job matching.<br>
Monitored and enhanced data quality within the data pipeline by utilizing key performance metrics and statistical methods.</p>
<p>Developed a video understanding model for an online learning platform.<br>
Incorporated a Temporal Shift Module, eye gaze detection, and behavior detection.<br>
Resulted in a 15% increase in engagement prediction accuracy over traditional methods.<br>
Orchestrated an automated data processing and hyperparameter tuning pipeline using Python and TensorFlow.<br>
Leveraged AWS services such as S3 and EC2.<br>
Resulted in a 10% improvement in model performance.<br>
Developed a Flask-based backend for an Android app.<br>
Served as the primary platform for product testing and demonstration.<br>
Worked closely with the frontend team to achieve real-time attention level detection through efficient WLAN communication and robust API connectivity.</p>
<p>Developed a data processing pipeline in Python and TensorFlow for a luggage security model that identifies hazardous items in X-ray images.<br>
Significantly reduced the development and tuning time for the model.<br>
Established robust data quality assurance through testing algorithms and monitoring.<br>
Implemented data augmentation techniques, including geometric transformations and color adjustments.<br>
Applied transfer learning with pre-trained models like YOLO and ResNet.<br>
Fine-tuned their last layers to significantly enhance the model's performance in the face of limited training data.<br>
Explored anomaly detection using a Reconstruction-Based Convolutional Autoencoder.</p>
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