• Spearheaded the application of advanced data analysis techniques, utilizing regular expressions(regex), spaCy, and Yake, to extract critical pricing data and pinpoint risk-related keywords.• Integrated natural language processing (NLP) techniques, including spaCy, to enhance the accuracyand efficiency of critical pricing data extraction.• Realized a notable 40% increase in profit margins through the application of data-driven insights,guiding strategic trading decisions.• Employed Azure Sentiment Analysis and Opinion Mining to evaluate trader sentiment and analyze theimpact of external factors on trading. Resulted in an impressive 15% enhancement in decisionaccuracy.• Collaborated closely with a dynamic team to conceive and implement robust risk managementstrategies, leading to a commendable 25% reduction in overall portfolio risk.• Meticulously managed and maintained a SQL database for comprehensive tracking of trade signals,executed trades, and profitability. This initiative resulted in a significant 30% reduction in dataretrieval times.• Implemented NLP-driven enhancements in the SQL database management, improving the tracking oftrade signals and profitability.• Successfully deployed the project on AWS EC2, enhancing scalability and streamlining datamanagement. Utilized AWS RDS for a high-performance relational database, achieving a remarkable40% improvement in data access.

•Developed an Android application using Kotlin, leveraging Augmented Reality and Machine Learningtechniques for document customization.•Reduced errors by 90% through the implementation of a Machine Learning-based error detection andcorrection system.•Implemented an application utilizing OCR (Optical Character Recognition) and NLP (NaturalLanguage Processing) to scan racers' IDs and retrieve relevant information for AMG track races.•Enhanced information retrieval accuracy and efficiency using NLP models for context understandingand data extraction.•Conducted extensive data collection and preprocessing to train a Deep Learning model for occupantsafety, addressing n-cap regulations.•Utilized TensorFlow and PyTorch frameworks for model development and optimization.•Successfully ported the Deep Learning model to a Jetson Nano for edge computing and real-timesafety analysis.

•Developed a user-friendly dashboard for service administrators to efficiently manage employeeauthentication.•Implemented the ability for administrators to securely upload and manage employees’ PEM files utilizing Amazon AWS services.•Played a major role in the backend team in developing an e-commerce website using Django.

•Implemented a face recognition application incorporating Eigenfaces, Fisherfaces and LBHP algorithmsachieving a 70% accuracy.•Utilized advanced facial recognition algorithms to optimize the application’s performance with 90% accuracy.