Projects using Azure SQL DatabaseProjects using Azure SQL DatabaseMyCarrier: DevOps & Cloud Transformation for Scalable Logistics SaaS
Preview description:
We helped MyCarrier move toward cloud-ready DevOps: adapted the system for microservices, implemented CI/CD and test automation, moved workloads to Azure, achieved 99.9% availability, decreased update delivery time to minutes.
Full description:
Problem
MyCarrier was growing fast, and the monolithic codebase became harder and more expensive to maintain. It was heavy, resource-consuming, and less suitable for the client’s long-term scaling plans. The team also needed faster delivery processes, but changes in the codebase and cloud migration had to stay unnoticed by users. Their logistics workflows could not be interrupted.
Solution
IT Craft aligned the project infrastructure with a microservices architecture and prepared the system for cloud operation. The team started with a technical audit and developed the migration strategy consisting of 20-28 items. The process was divided into pre-migration, migration, and post-migration phases, thus the product could keep running while infrastructure changed step by step.
The team moved non-critical standalone jobs to the cloud, connected virtual machines to Azure Storage Account, moved logging to MongoDB Atlas, and transferred user data and related processes to the cloud. New app functionality was released as microservices. IT Craft also designed and fine-tuned a CI/CD pipeline, automated infrastructure deployments, and reduced update delivery time through regression test automation.
Results
· Achieved 99.9% system availability.
· Maintained high fault tolerance using three shared clusters.
· Reduced update delivery time from hours to minutes.
· Enabled Sprint focus changes every two weeks based on client priorities.
Tech Stack:
· Azure Cloud
· MongoDB Atlas
· Azure SQL
· Azure App Service
· Azure Functions
· Azure Storage Account An AI-driven trading engine that analyzes 200-DMA breakouts and real-time market sentiment to generate long/short recommendations. It scans 1,500+ NSE stocks in under 2 seconds and uses generative AI to build option strategies, delivering event-driven trade signals end-to-end. I built the full Django backend, async APIs, and the signal-generation logic.
Accomplishments and responsibilities:
Built an AI-driven trading engine analyzing 200-DMA breakouts and market sentiment, generating long/short signals with ~70% directional accuracy — outperforming baseline strategies by 35%;
Integrated generative-AI insights for automated option-strategy creation (spreads, straddles, condors), improving Sharpe ratio by 1.6× and cutting manual analysis time by 60%;
Developed a Django backend with async APIs scanning 1,500+ NSE stocks in under 2 seconds, achieving 40% lower latency with event-driven alerts. Practical Golf is a premium golf education and lifestyle brand focused on helping players improve performance, lower scores, and enjoy the game through expert-driven content and modern digital experiences. The brand combines trusted instruction, product insights, and community engagement for golfers who value both skill development and a smarter approach to the game.
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Overall, Practical Golf represents a growth-focused sports media brand dedicated to improving the modern golfer’s experience through education, trusted content, and premium digital presentation. 
