Freelance Fullstack Engineers in Uttar PradeshFreelance Fullstack Engineers in Uttar Pradesh
Senior Product Designer & Full-Stack Partner
$25k+
Earned
11x
Hired
4.9
Rating
161
Followers
Senior Product Designer & Full-Stack Partner
Full Stack Developer | MVPs, SaaS & Dashboards for Startups
$10k+
Earned
5x
Hired
4.9
Rating
24
Followers
Full Stack Developer | MVPs, SaaS & Dashboards for Startups
Building MVPs for startup founders with clean UX in a week.
1x
Hired
5.0
Rating
60
Followers
Building MVPs for startup founders with clean UX in a week.
🌟Top Full stack Dev Crafting OUTSTANDING Web & Mobile Apps
5.0
Rating
6
Followers
🌟Top Full stack Dev Crafting OUTSTANDING Web & Mobile Apps
Building Trust with DevOps & Fullstack Expertise 🛠️
Building Trust with DevOps & Fullstack Expertise 🛠️
Custom ERP & CRM Developer | Automating Schools & Businesses
New to Contra
Custom ERP & CRM Developer | Automating Schools & Businesses
Aspiring dev crafting practical, reliable systems.
New to Contra
Aspiring dev crafting practical, reliable systems.
Cover image for Government School ERP System —
Government School ERP System — Full-Stack Platform for a Real Government School Client Designed and built a bilingual, full-stack ERP system for a government school in Uttar Pradesh — replacing paper attendance registers, handwritten admission forms, and manual Mid-Day Meal logs with a secure, role-based digital platform. Architected the full data layer — a 25+ model relational schema (PostgreSQL + Prisma) mapped directly from the client's actual government paperwork, including India's UDISE+ admission forms and Form-34 Transfer Certificates, not a generic template. Built defense-in-depth security — role-based access control enforced at the server layer (not just route guards), brute-force account lockout, and a login system designed around a real constraint: students without personal phones authenticate by admission number instead. Shipped a bilingual UI from day one — Hindi and English throughout, using next-intl, so the interface actually works for the people using it, not just the developer testing it. Automated the admission pipeline end-to-end — a 30+ field digital intake form (photo + document uploads to cloud storage) that auto-generates student accounts and credentials in a single atomic transaction. Still actively building — Mid-Day Meal tracking and automated report card generation are next. Open to freelance and contract work where someone needs to own both the system architecture and the day-to-day execution. If it's real, complex, and needs to actually ship — let's talk.
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39
Cover image for Deepfake Detection System
Problem: Deepfake accessibility
Deepfake Detection System Problem: Deepfake accessibility creates real risks—misinformation, identity fraud, digital trust erosion. I built a production-grade detector: fast, accurate, explainable, CPU-deployable. Approach: Transfer learning with ResNet-50 (25.5M params, pre-trained ImageNet-1K V2). Rather than training from scratch on limited data, I leveraged rich hierarchical features that transfer exceptionally well to detecting artifact patterns (blending boundaries, color mismatches, compression artifacts). Key Decisions: Face-Centric Preprocessing: OpenCV DNN crops faces to 224×224, concentrating attention on artifact-rich regions (eyes, mouth, jawlines). Custom Classification Head: Bottleneck design (2048→512→1) with Focal Loss, label smoothing, progressive unfreezing—prevents overfitting on ~4,000 Ciplab images. Explainable AI: Custom Grad-CAM generates attention heatmaps showing exactly where the model detected artifacts. Real-World Robustness: Trained on JPEG compression, Gaussian noise, blur augmentations to handle compressed video feeds. Results: AUC-ROC: 0.9424 | Accuracy: 87.25% | Precision/Recall: 87.27%/84.81% Inference: <100ms per face on CPU Live on Hugging Face Spaces (no GPU) Tech: PyTorch, OpenCV, Albumentations, Gradio. Images, videos, live webcam—one codebase. Demonstrates: End-to-end AI engineering with deliberate trade-offs (efficiency over raw accuracy), training discipline (Focal Loss, Cosine Annealing), and production-first thinking (CPU compatibility, interpretability, real-world robustness). Demo: https://huggingface.co/spaces/Shri04/deepfake-detector
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