Career Profile
Backend Developer experienced in building robust microservices and web applications, adept at integrating data from various sources and third-party APIs to streamline processes and enhance functionality. Proven ability to design and implement scalable solutions to meet business requirements. Passionate about leveraging technology to drive innovation and deliver exceptional user experiences.
Experiences
- Architected a suite of REST APIs in Java to perform seamless CRUD operations over NoSQL database.
- Engineered a JSON-based solution to enable defining custom data classification model. Complemented by a POC application developed in React and TypeScript.
- Order Management Service - (Backend microservice)
- Developed Low Level Design following SOLID principles and design patterns like Factory, Builder and Singleton.
- Devised REST APIs and webhooks using Java Spring Boot and MySQL ensuring idempotency for transactions.
- Created exhaustive unit and integration tests using Jacoco library to maintain a code coverage of over 90%.
- Sales Sync Service - (Event-driven backend microservice)
- Engineered a robust microservice with AWS Step Functions workflows, implementing an event-driven architecture for seamless synchronization of offline sales data from Salesforce to the OMS.
- Designed workflows for offline sales creation and updation events, implemented in a Java Spring Boot application.
- Improved data accuracy by 60% and reduced human effort by 90% through synchronization workflows.
- Integrated SAML authentication in Python Django application through REST APIs for customer onboarding.
- Performed stress and load testing on 5 backend microservices using Locust to measure throughput and latency.
- Data Warehouse Pipelines - (Custom ETL Application for data warehousing)
- Spearheaded a 3-member cross-functional team overseeing daily scrum meetings and progress tracking in building ActiveMQ based events processing microservice with high reliability and scalability.
- Implemented pessimistic locking for data consistency. Pipeline is capable of processing books with 2 million events.
- Modernized the deployment process by creating a sophisticated CI/CD pipeline utilizing GitHub Actions, reducing the deployment time by 60%.
- Utilizing caching, asynchronous calls and batching, improved application performance by over 30%.
- Exposed REST endpoints to pause or resume Spring Batch job thus reducing failure handling effort by 40%.
- Analysed business requirements and enhanced RDBMS schema of Java web application by extending base product.
- Integrated third party SOAP APIs to fetch credit information making use of token based authentication.
- Added an auditing capability leveraging the change event in jQuery for seamless tracking of data changes.
Projects
Face Recognition as a Service
- A web app which accepts POST requests with images and returns face recognition result as text in response (IaaS and PaaS Architecture).
- Created a multi-threaded Python Flask application on AWS EC2 along with essential S3 buckets and SQS queues.
- Replicated auto-scaling behavior in Python script to dynamically provision up to 20 instances from saved AMI.
- Shifted from IaaS to PaaS architecture using AWS Lambda, eliminating the auto-scaling script dependency.
FitLife
- Android application
- Created an android application using Kotlin to suggest personalized workouts as per user preferences and history.
- By leveraging coroutines, we significantly enhanced the transition between different activities in the Android application, resulting in a smoother and more seamless user experience.
- Developed fuzzy logic to generate user rating and suggest workouts. Added capability to record user feedback.
- Used camera and accelerometer sensor in the smart phone to measure metrics like heart and respiratory rate.
Glucose Data Analysis & Clustering
- Machine learning model for use in Glucose Monitoring machines.
- Developed a Python script for extracting Time Series data from Continuous Glucose Monitoring (CGM) and insulin pump datasets.
- Implemented a feature extraction pipeline and generated a machine learning model to derive valuable insights for optimizing diabetes treatment strategies.
- Designed and executed a clustering algorithm to categorize data patterns. Evaluated the accuracy of clustering using key metrics, including Sum of Squared Errors (SSE) and supervised cluster validity metrics.
Habitability Prediction of Exoplanets
- Machine Learning model to predict habitability of an Exoplanet
- Performed data pre-processing and Exploratory Data Analysis (EDA) on PHL data for feature selection.
- Implemented a collection of Machine Learning models like SVM, Logistic Regression, KNN, K-Means on the pre-processed data.
- Compared the model prediction accuracy using metrics like Accuracy, Precision, Recall and F1 Score. Achieved an average accuracy of 97%.
Skills & Proficiency
Programming Languages: Java, Python, C++, C, SQL, Kotlin, TypeScript, JavaScript, R, Scala, Bash script
Databases: MySQL, Redis, MemCached, NoSQL, PostgreSQL, MongoDB, SQLite, MSSQL
Frameworks: Spring Boot, React, Flask, Locust, Srping MVC, Django, OpenFaas, Node.js, JUnit
Tools: Docker, Git, Maven, Gradle, Jenkins, Postman, SoapUI, Minikube, IntelliJ, JupyterLab, Sonar
Libraries: Scikit-learn, Jacoco, NumPy, Pandas, Matplotlib, JDBC, Mybatis, Hibernate-ORM
AWS Technologies: Lambda, EC2, ASG, SQS, S3, RDS, DynamoDB, Athena, Step Functions, IAM
Technologies: ActiveMq, Kubernetes, REST, Kafka, RabbitMq, gRPC, SOAP, Ceph, ZooKeeper, Heroku, OOPs