Pranav Shinde
Computer Vision Engineer | ML Systems & MLOps
Badawe Engineers (Defence R&D)
AI Engineer - Computer Vision & ML Systems
- Built and maintained 500k+ image annotation pipeline with custom SAM 2.1 boundary refiner for automated mask correction.
- Trained 13-class YOLO instance segmentation model (mAP50~0.74) with WeightedDetectionLoss for severe dataset imbalance.
- Engineered dual-GPU training pipeline (RTX 5080/3060) resolving CUDA conflicts, reducing validation turnaround by 18%.
- Migrated inference PyTorch → ONNX with batch processing, achieving 78% memory reduction (9.2GB → 2GB).
- Automated full annotation workflow via CVAT REST API, replacing hours of daily manual effort.
Vital Vistara
Full Stack Intern (ML Backend)
- Designed AWS infrastructure for ML workloads: VPC, RDS, Lambda for serverless inference, S3 for artifacts.
- Implemented CI/CD with Docker builds, ECR/ECS Fargate deployment, and automated ML model versioning.
- Deployed scalable, cloud-native applications on AWS, achieving 99.9% availability.
Oneqid Technologies, IIT Delhi Research and Innovation Park
Web Development Intern
- Migrated legacy infrastructure to Next.js, resulting in a 35% increase in page load performance.
- Implemented automated Vercel deployment workflows, boosting Lighthouse performance scores from 65 to 92.
Sep 2024 - 12/2024
Full Stack Engineer | Engineered secure sandboxed code execution using Docker with CPU/memory limits on AWS ECS Fargate. Implemented PostgreSQL connection pooling (PgBouncer), reducing connection overhead by 70%. Built scalable backend for supporting 1000+ concurrent users with strict security isolation.
Aug 2024 - Oct 2024 • Top 10 National Finalist, Smart India Hackathon 2024
Backend & ML Engineer | Deployed on-device pose estimation (Google ML Kit) on Android for low-latency, privacy-preserving fitness evaluation. Engineered Firebase-backed pipeline for nationwide athlete profiling and leaderboards supporting 50,000+ competing teams nationally.