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Hello, I'm

Tarun Kumar Pothineni

Full Stack Developer, Java Developer

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About Me

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Experience

2.5+ years
Full Stack Engineer

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Education

Master's in CS - State University of New York at Binghamton
Bachelor's in ECE - LNMIIT

Full-stack developer with 2.5 years of experience and currently pursuing an MS in Computer Science at SUNY Binghamton. Proficient in Java, Spring Boot, React, and cloud technologies like AWS and Azure. Experienced in building microservices, real-time data pipelines with Kafka and Spark, and ML. Skilled in DevOps, CI/CD, and secure, test-driven development.

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Explore My

Skills

Programming Languages, Testing, Databases and Methodologies

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Java

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Python

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C

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C++

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HTML

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CSS

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R Programming

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MySQL

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PostgresSQL

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Oracle

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VoltDB

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SDLC

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Agile

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Waterfall

Cloud Technologies, Version Control Tools, CI/CD and DevOps Tools

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AWS

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EC2

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Git

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Github

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Jenkins

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Docker

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Kubernetes

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Bitbucket

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Kafka

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Grafana

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Kibana

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Terraform

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Prometheus

Frameworks and Developer Tools

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Spring Boot

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Spring

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Hibernate

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React

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Angular

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Maven

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RESTful API

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gRPC

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V S Code

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Eclipse

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Intellij

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Postman

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DBeaver

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Office 365

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Explore My

Experience

Senior Software Developer

Comviva Technologies Limited. (Dec 2022 - Jul 2023)

  • Spearheaded the successful migration of 23 microservices from VoltDB to a relational database system (RDBMS), resulting in a 66% reduction in infrastructure costs and a 15% improvement in overall query performance.
  • Centralized services via Consul, reducing deployment effort by 35% and improving system reliability across environments.
  • Engineered a modular, fault-tolerant offer distribution system using Kafka for real-time processing with near-zero downtime.
  • Conducted in-depth performance profiling and JVM tuning, improving system throughput by 38% under peak loads.

Software Developer

Comviva Technologies Limited. (Jul 2021 - Nov 2022)

  • Refactored legacy codebase into modular, cloud-native microservices using Spring Boot, Docker and Kubernetes, resulting in an 18% increase in deployment frequency and simplified CI/CD automation across environments.
  • Implemented a RESTful APIs to exclude high-propensity users from promotional offers, boosting client revenue by 17%.
  • DDeveloped real-time Grafana dashboards for end-to-end system health monitoring and integrated proactive alerting with Slack and Splunk, cutting incident resolution time by 45% and improving operational visibility across teams.
  • Deployed the EFK stack for log management, improving debugging efficiency and reducing log analysis time by 40%.
  • Optimized PostgreSQL queries, reducing response latency by 600ms and improving data fetch times by 20%.

Software Developer Intern

Comviva Technologies Limited. (Jan 2021 - Jun 2021)

  • Built a RESTful microservice to streamline offer distribution, reducing response time by 40% for over 15,000 daily users.
  • Boosted code reliability by achieving 80% unit test coverage through TDD, reducing production bugs by 25%.
  • Resolved over 300+ real-time bugs during deployment, significantly enhancing system stability and reducing release issues.
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Browse My Recent

Projects

DriveSync

Java ,Sprinboot ,Gradle ,React ,Restful APIs , OAuth2 ,Spring Security ,JUnit 5 , Mockito

  • Built a full-stack Google Drive manager using React and Spring Boot with Google OAuth2 and JWT for secure access.
  • Developed RESTful APIs for CRUD operations on files with sub-300ms sync, ensuring a smooth user experience.
  • Implemented Spring Security with CSRF and OAuth2, automating auth and securing isolated Google Drive access per user.
  • Tested backend endpoints with JUnit 5, Mockito, and MockMvc, achieving 95% coverage.

HandWritten Digits Recognition

Python ,CNN ,TensorFlow Lite ,Java ,Android Studio

  • Developed an Android app using Java and TensorFlow Lite to predict handwritten digits with a custom CNN model, achieving 98.97% accuracy and reducing latency to 2ms.
  • Evaluated inbuilt models such as ResNet50, and MobileNetV3, achieving accuracies of 98.55% and 96.64%, respectively.
  • Implemented a robust machine learning solution with a 4-layer CNN, trained over 7 epochs using the Adam optimizer and dropout regularization, outperforming traditional models.

Enhanced YOLO Framework

Python ,PyTorch ,CUDA ,NumPy ,Pandas , Matplotlib ,Google Colab

  • Optimized the YOLOv7 model, improving mean Average Precision (mAP) by 3.1% and reducing inference time by 5.4%, enhancing detection speed and accuracy on the Pascal VOC 2012 dataset.
  • Increased model precision by 2.4% and recall by 1.8% through hyperparameter tuning and efficient data preprocessing.
  • Reduced GPU memory usage by 4.5% and enhanced overall model efficiency, accelerating training cycles on Google Colab with a T4 GPU.
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Get in Touch

Contact Me

Mail

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+1 (607) 484- 7831

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LinkedIn

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