Bakar Qureshi
AI engineer building RAG pipelines and ML-powered apps that
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I built an AI system that detects solar panel faults from a single image โ and deployed it with a live demo.
Here's what it does:
๐ฒ ๐ณ๐ฎ๐๐น๐ ๐ฐ๐น๐ฎ๐๐๐ฒ๐: Bird-drop, Dusty, Snow-Covered, Electrical Damage, Physical Damage, Clean
ย โข ResNet101 fine-tuned on solar panel imagery
ย โข 96.3% training accuracy with strong per-class F1
ย โข REST API deployed on Hugging Face Spaces (Docker + Flask)
ย โข React frontend with real-time confidence scores
๐ช๐ต๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ๐:
According to Raptor Maps' 2025 Global Solar Report, equipment-driven underperformance has increased 214% since 2019 โ resulting in $10 billion in lost energy value in 2024 alone. The average solar site loses 5.77% of its power output annually, costing ~$5,720 per MWdc.
The use cases that excite me most:
ย โข Drone inspection over utility-scale farms
ย โข Ground robotics for close-up panel analysis
ย โข Direct integration into SCADA systems via REST API
๐๐๐ถ๐น๐ ๐๐ถ๐๐ต: PyTorch ยท ResNet101 ยท Flask ยท Docker ยท HuggingFace Spaces ยท React + Vite
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I built Mnemo โ a RAG system.
Upload your docs, organize them into projects, ask questions โ get answers grounded in your files, with the exact page + match confidence shown inline.
Stack:
ย โข FastAPI
ย โข React (Vite)
ย โข JWT + httpOnly cookies for authentication
ย โข LangGraph
ย โข pgvector (PostgreSQL) โ stores embeddings, powers semantic retrieval
ย โข Groq
Deployed on AWS:
ย โข EC2 โ FastAPI backend behind Nginx
ย โข RDS โ managed PostgreSQL running pgvector
ย โข S3 โ hosts the built React frontend
ย โข CloudFront โ CDN serving the frontend to users
Try it yourself: https://lnkd.in/dtrjYeeT
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