Developed and optimized a Retrieval-Augmented Generation (RAG) LLM application to automate contract analysis for a Fortune 500 infrastructure services client, leveraging advanced NLP techniques and vector-based search to improve clause detection accuracy and reduce manual legal review time.

Designed and implemented a CI/CD-enabled system integrating Retrieval-Augmented Generation (RAG), fine-tuned LLMs, Pinecone vector database, and LangChain to extract information from engine documents in a generative AI-powered chatbot. This solution significantly improved efficiency for internal users involved in engine buying processes by enabling seamless information retrieval and contextual understanding. Developed a machine learning model leveraging internal engine component data and external sources to predict component demand, optimizing inventory strategies and enhancing decision-making. Additionally, utilized Power BI and SQL to create a comprehensive report integrating BTS flight and weather data, demonstrating potential cost savings for airlines through AerSale’s AerAware product, ultimately driving increased revenue by showcasing its impact on pilot vision and operational efficiency.

Developed a Deep Learning-based process monitoring system using an LSTM-Autoencoder for real-time anomaly detection in multivariate sensor data, enhancing equipment availability and preventing damage to expensive workpieces. The model was extended to monitor multiple identical CNC machines, achieving an average F1-score of 0.96 across various scenarios. Deployment was streamlined using AWS SageMaker for model training, AWS IoT for data ingestion, and CI/CD pipelines with AWS CodePipeline to ensure continuous improvement and deployment efficiency.

Developed cutting-edge loss weighting methods for multi-task learning in autonomous driving, achieving superior performance over industry benchmarks. Extensive evaluations were conducted on scene understanding tasks, including semantic segmentation, object detection, depth estimation, and surface normal estimation, leveraging datasets like Cityscapes and NYUv2 to establish a robust evaluation framework. These novel methods were seamlessly integrated into the existing autonomous driving software, ensuring compatibility and performance optimization. The research was published in GCPR 2024, contributing to advancements in neural networks and autonomous driving, with two registered patents recognizing the innovation and solidifying its intellectual property value.

Led the internal Business Intelligence development team as a Scrum Master, fostering efficient and collaborative processes for successful BI initiatives. Designed and implemented streamlined data ingestion workflows for the staging area and raw data vault using AWS services such as S3, Lambda, and Batch, improving efficiency and reliability. Collaborated on a Tableau-based data visualization project to analyze customer engagement and optimize marketing strategies for a leading e-commerce client, contributing to a 15% increase in conversion rates within six months.