Vraj Nena

Versatile technology professional combining expertise as a:

Software Engineer

Building scalable applications and microservices with modern technologies and best practices

Data Consultant

Implementing CI/CD pipelines and infrastructure automation to streamline development workflows

Cloud Engineer

Designing robust cloud architectures and optimizing system performance across platforms

Proven experience with Infrastructure as Code, containerization, and cloud platforms. Specialized in improving system performance and reducing operational costs through efficient automation and architectural solutions.

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Professional Experience

Software Engineer

MineExcellence

Aug 2024 - Present

Leading the development of innovative solutions for mining operations, focusing on data processing and visualization:

Developed a RESTful API using FastAPI to process large point cloud files, integrating with Potree library for advanced stope visualization in React

Implemented infrastructure as code using Terraform, deploying production-ready FastAPI services capable of handling 100+ concurrent requests

Achieved 99.9% system uptime through comprehensive monitoring and logging solutions for LiDAR data processing pipeline

Created functional requirements for blast stope recovery, collaborating with development and mining teams to ensure best practices

Python FastAPI React Terraform AWS LiDAR Processing

Featured Projects

Gambling Prevention Website

View on GitHub
React Django Redis Celery Azure AI

A distributed system for mental health data analysis focusing on gambling prevention. The platform features:

  • ETL pipelines and Apache Airflow for data warehouse management
  • CI/CD pipelines for automated deployment across environments
  • Redis and Celery workers for asynchronous task processing
  • Interactive 3D visualization with Azure AI Speech API integration
  • Robust data security measures for sensitive health information

Object Detection Platform

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Kubernetes Docker Terraform React ML

A scalable platform for object detection using machine learning models deployed on Kubernetes. Key features include:

  • VM configuration using Terraform and Ansible on Oracle Cloud
  • Containerized machine learning models deployed on Kubernetes
  • React frontend containerized and deployed on Kubernetes
  • Load testing with Locust supporting 10+ concurrent users
  • Advanced Linux configuration for Kubernetes cluster setup

Melbourne Trains Dashboard

View on GitHub
Apache Kafka Apache Spark Power BI GCP

A real-time dashboard for Melbourne's metro train system featuring:

  • Real-time train location tracking using GTFS data
  • Apache Airflow for scheduled data fetching
  • Apache Kafka and Spark for stream processing
  • Infrastructure deployment on GCP using Terraform
  • Live statistics for train delays and disruptions

Let's Connect

Interested in collaborating or discussing opportunities? Get in touch!

Contact Me