Devansh Mudgal

Backend / Platform Engineer with hands-on experience building scalable cloud-native systems, microservices, and production-grade backend applications using Python, Django, Docker, and AWS. Experienced in infrastructure optimization, deployment automation, and multi-region production operations. Strong foundation in backend engineering, distributed systems, DevOps practices, and cloud infrastructure, proven impact in reducing operational costs, and improving reliability.

Platform Engineer at Detect Technologies (2024-10 – Present)

• Built Sentinel, an asyncio-based service processing 750GB/day of application logs from RabbitMQ, using pandas and ADTK-based anomaly detection to flag abnormal log-volume drops and error spikes; reducing false positive alerts by 30% and MTTR by 20%.
• Migrated project's architecture from traditional multiple VMs to AWS Lambda; reducing cloud costs by 30% and achieving auto-scaling of microservices.
• Engineered a cost-centric migration of storage (S3 to MinIO) and compute (EC2 to Oracle VMs), reducing annual cloud expenditure by 50% while maintaining 99%+ uptime for multi-region deployment flows.
• Owned and managed enterprise video processing pipeline and software deployment across 9+ global regions (US, Canada, EMEA, India, Singapore, Australia), and coordinated across 5 teams for go-live readiness.
• Optimized deployment pipelines by dockerizing Django-based applications and orchestrating services with Docker Compose and Docker Swarm, resulting in efficient configuration management, and scalability; ensuring 99%+ Production uptime.
• Revamped a motion-based optimization algorithm in video processing, reducing unnecessary processing and saving 3000+ hours of manual effort across 2 deployments.
• Contributed to multiprocessing image/video processing pipelines with 3 team members and added timeout handling, AWS CloudWatch logging, and alerting, reducing SLA from 30 minutes to under 20 minutes (33% improvement).
• Expanded a MinIO lifecycle service with hot-to-warm and warm-to-cold workers, processing 900K+ to 1.8M+ objects with configurable retention, retries, dry-run mode, and Dockerized deployment.
• Designed frontend components using Angular Material library and Figma to enhance the user experience for client dashboard.

Bachelor of Technology in Computer Science and Engineering with specialization in Bioinformatics – Vellore Institute of Technology (2020-09 – 2024-08)