Abhay Joshi
Ashburn,VA
Summary
Detail-oriented software engineer with a strong background in computer software engineering. Has extensive experience in developing and optimizing complex systems and applications using a variety of programming languages and technologies. Proficient in Python, Java, swift, JavaScript, and experienced with SQL, PySpark, and Azure ML.
Certified AWS Cloud Practitioner with strong analytical thinking, problem-solving, and project management skills. Published several articles on Agile methodology, requirement management, and architectural decisions. Committed to continuous learning and improvement in software engineering and data science.
Overview
5
5
years of professional experience
Work History
Software Engineer
Swasti Software Inc.
02.2024 - Current
- Developed React.js-based front end with Material UI and TailwindCSS, resulting in 30% increase in user engagement.
- Performed A/B testing on developed product using Google Analytics, leading to 15% improvement in conversion rates. Proposed features such as shopping cart, trending items section.
- Actively participated in system design of features (shopping cart, trending items section, most bought items section) using Azure Machine Learning for predictive analytics, enhancing personalized content recommendations by 20%.
- Utilized SQL to query and process large datasets for product sales data, visualized using Tableau to ensure high responsiveness and user-friendly interface, reducing report generation time by 40%.
- Created complex SQL queries to extract and analyze data for A/B testing, improving data accuracy by 25%.
- Integrated SQL-based data retrieval into front end to dynamically display trending items, most bought items, and other key metrics, increasing page load speed by 35%.
- Developed and optimized SQL queries to improve performance of data-driven features within shopping cart and trending items section, reducing query execution time by 50%.
Software Analyst
Osmania General Hospital
07.2021 - 07.2022
- Architected and developed a distributed data repository to store hospital information, ensuring high availability and scalability. Designed and implemented RESTful APIs using Spring Boot and utilized Spring Data JPA for efficient CRUD operations.
- Implemented robust data validation and error handling mechanisms to ensure 100% data integrity, facilitating easier debugging and maintenance of data quality standards.
- Integrated authentication and authorization using Spring Security, including role-based access control for three user types (admin, doctor, nurse), to secure sensitive data and maintain consistent database transactions.
Machine Learning Engineer
Digithon
07.2020 - 06.2021
- Employed Python for exploratory data analysis, data pre-processing (tokenizing over 100,000 text records), and feature engineering (bag-of-words on 50,000 text samples) on "Pharmacy Text Analytics" dataset, enabling effective text classification tasks.
- Implemented Python scripts and Unix commands to experiment with various machine learning algorithms, including Naive Bayes, Logistic Regression, SVM, RNNs, and Transformers (BERT), on pre-processed dataset. Optimized performance by tuning hyperparameters across 10,000 iterations, achieving an F1-score of 0.82 on test set.
- Integrated trained text classification model (with 79% accuracy) into a Spring Boot application using JSON payloads and RESTful APIs. Utilized Apache Kafka for asynchronous communication, developed a React.js frontend interface to accept and display predictions.
Machine Learning Engineer
The Smart Bridge
08.2019 - 06.2020
- Automated collection of a diverse dataset of over 100,000 human face images using Python scripts. Performed data pre-processing, including resizing 20,000 images, normalization, and data augmentation techniques (flipping, rotation, scaling) to increase training dataset size by 30%.
- Configured Python virtual environments and installed packages like Keras, TensorFlow, OpenCV, NumPy, Pandas, Matplotlib, and scikit-learn. Designed a Convolutional Neural Network (CNN) architecture with 5 convolutional layers, 3 pooling layers, and 2 fully connected layers, optimized using regularization techniques and hyperparameter tuning across 1,000 iterations.
- Leveraged Google Cloud Platform and Google Colab for GPU acceleration, speeding up training process by 10x.
- Trained CNN model on pre-processed dataset of 80,000 images, implementing techniques like early stopping, checkpoint saving, and learning rate scheduling. Achieved accuracy of 82% and F1-score of 0.80 on test set.
Education
Master of Science - Software Engineering
George Mason University
Fairfax, VA12.2023
Skills
- Coding Languages: Java, Swift, Python
- Software: snowflake, Splunk, Docker, Kubernetes, AWS, Git, GitHub
- Databases: PostgreSQL, Oracle MySQL, Cassandra DB
- Machine learning: Statistics, NLP, Computer Vision, Prompt Engineering, ML packages ( Keras, tensorflow, opencv, numpy, pandas, matplotlib, scikit-learn)
- Design Patterns: Micro Services, Singleton, Abstract Factory, MVC, Front Controller, Object Oriented Design, RESTful
Timeline
Software Engineer
Swasti Software Inc.
02.2024 - Current
Software Analyst
Osmania General Hospital
07.2021 - 07.2022
Machine Learning Engineer
Digithon
07.2020 - 06.2021
Machine Learning Engineer
The Smart Bridge
08.2019 - 06.2020
Master of Science - Software Engineering
George Mason University
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