Certificate

Built and optimized RESTful APIs using Python and Django and architected a centralized event-driven notification and alerting system (Email, Slack, Push, In-app) with Celery, Redis, RabbitMQ and FCM, reducing API response time by 38%.
Implemented asynchronous background processing using Celery, Redis and RabbitMQ to offload long-running tasks, reducing request blocking by 40% and validating execution flows via Redis Insight and RabbitMQ UI.
Enhanced AI defect detection pipelines by introducing credit validation, GPU usage tracking and add-on credit management, improving resource utilization and reducing model execution failure rates by 25%.
Architected Analytics APIs for inspection trends, defect heatmaps, component-wise metrics and designed scalable CSV/PDF reports using Jinja2 for 100K+ records, improving system visibility and model training speed by 20%.

Created Multi-Client Platform: Designed and built three distinct clients (for restaurants, customers, and delivery personnel), ensuring smooth functionality and interaction across all users of the platform, serving over 500 active users.
Real-Time Order Tracking: Integrated WebSocket to provide real-time order updates, allowing seamless communication between customers and restaurants, enhancing the user experience and operational efficiency.
Achieved Significant Milestones: Successfully processed over 700 orders and generated approximately 3 lakh in revenue through the platform.

Designed and developed software tools to streamline aviation system workflows, improving efficiency and ensuring adherence to quality standards.
Created software models to simulate aviation system performance, including the Optolocator System (OLS) and UAVs, enabling data-driven decision-making and precision testing.
Collaborated closely with industry experts to enhance aircraft functionality, with a particular focus on optimizing the Optolocator system (OLS) and Unmanned Aerial Vehicles (UAVs).

Experience