— Experienced in Backend, Cloud & Distributed Systems

Experienced in building scalable chat apps with Socket.io, Redis, MongoDB, React, and HAProxy. Proficient in cloud automation using Pulumi, AWS, and GCP services for infrastructure deployment and management. Led initiatives to optimize code quality and deployment efficiency using Kafka, Amazon SQS, S3, Load balancer, FastAPI, VPC, and Lambda triggers. Skilled in DevOps practices, achieving code coverage through Unit and Integration Testing

— Technical Skills

-Languages: Python, Java, C++, Dart, JavaScript, Typescript, SQL
-Frameworks: Node.js, Spring Boot, Flutter, Django, Flask, ReactJS, Vue.js, NextJS, Angular
-Cloud Infra AWS, GCP, Kubernetes, Terraform, Docker, Ansible, Linux, Git, Jenkins
-Protocols SMTP, UDP, gRPC, HTTP, TLS, TCP/IP, DNS, DHCP
-ML/Big Data Kafka, RabbitMQ, Redis, Amazon S3, SQS, Apache Spark, Hadoop, Elasticsearch

— Work experience

Software Engineer Co-op

Prime Medicine / July 2023 - Dec 2023

• Deployed Serverless AWS ECS pipeline, ALB, VPC, ECR, & CloudWatch orchestrating CDK Stack from scratch

• Built File Parser Tool triggering AWS Lambda using SQS and S3 buckets reducing 50% operational overhead 

• Configured VPC endpoints, subnets with security groups, AWS ALB with SSL certificate, and CloudWatch for logs resulting in 30% reduced infra cost

• Developed a FastAPI app with RESTFul APIRouter endpoints, Docker, OAuth, leveraging Benchling SDK

• Build CI/CD GitHub Actions pipelines and increased Code Coverage to 85% employing Unit and integration tests using PyTest, DocTest, and Unittest modules

Full Stack Developer

Tata Consultancy Services / Aug 2019 - Dec 2021

• Developed migration framework using Java, J2EE, Core Spring 3.0, Hibernate 3.0, Web Services, SOAP API

• Implemented Kafka Topic payment events leveraging Spring Boot's SAGA Pattern with Hibernate ORM, improving 40% transaction resilience

• Orchestrated Kubernetes, Elasticsearch, Zipkin distributed tracing resulting 15% improved scalability & indexing

• Implemented Spring Security Authorization, GraphQL APIs payment retrieval reducing 75% network overhead

• Build robust CI/CD pipeline on Jenkins and utilized JUnit, Mockito Framework for Unit & Integration testing

— Education

Northeastern University

Master's degree, Information Systems / 2022 - 2024
 

University of Mumbai

Bachelor's degree, Electronics & Telecommunications / 2015 - 2019
 

— Projects

Google Hackathon

Developed a revolutionary mental health AI social app using Flutter, Dart, dio networking, bloc state, seamlessly integrating Google and Apple Pay for convenient contributions / Feb 2024

Demo: https://devpost.com/software/caresync-8vxn3l

Github: https://github.com/NihalGajbhiye/GoogleHackathon

Distributed Chat Server

Employs Socket.io for real-time communication, Redis manages cache/pub-sub, MongoDB ensures persistence, HAProxy handles load balancing, and React boosts frontend interactivity for a scalable chat app / Jan 2024

Demo: https://vimeo.com/921546917?share=copy

Github: https://github.com/NihalGajbhiye/ConnectHub-Distributed-Chat-Server

AWS/GCP Infrastructure

Implemented Pulumi Python script automating AWS and GCP infrastructure deployment, managing VPC, RDS, EC2 auto-scaling, and integrating with SES and Route 53 for email notifications and DNS routing / Nov 2023

Github: https://github.com/NihalGajbhiye/Infrastructure-As-Code-AWS-GCP-Pulumi

LifeLine Enterprise Application

LifeLine is a user-friendly application dedicated to community welfare and facilitates seamless connections between volunteers, help seekers, donors, and professionals. Managed by the Lifeline Manager, our platform coordinates requests for assistance, transportation, medical aid, and data transmission to doctors. With the mayor's oversight and the support of supervisors and administrators, we ensure efficient operations to serve those in need. / Jan 2023

Github: https://github.com/NihalGajbhiye/LifeLine-Enterprise-Application
 

X-Ray-Image-Processing-Pneumonia-Prediction-Model

The deep learning model is built using the Keras Sequential API with the following architecture:

• Convolutional layers (Conv2D)
• Batch normalization layers (BatchNormalization)
• Max-pooling layers (MaxPool2D)
• Dropout layers (Dropout)
• Flatten layer
• Dense layers (Dense)

Github: https://github.com/NihalGajbhiye/X-Ray-Image-Processing-Pneumonia-Prediction-Model
 

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