LiDaR-based object detection & tracking for autonomous vehicles
Set up & configured Ouster LiDAR & SDK on Raspberry Pi, achieving 35 FPS with 95% detection accuracy
Muhammad Z, M is an AI Engineer specializing in machine learning systems, data engineering, and applied AI solutions with hands-on experience building production-grade ML pipelines and intelligent automation systems. He works extensively with Python-based AI ecosystems to develop and deploy machine learning and deep learning models for real-world applications, including healthcare automation, speech processing, and computer vision. His expertise spans data engineering and analytics using SQL databases such as PostgreSQL,…
Set up & configured Ouster LiDAR & SDK on Raspberry Pi, achieving 35 FPS with 95% detection accuracy
LangChain-based conversational AI agent integrated with production SQL & scheduling APIs, reducing appointment booking time
Fine-tuned a Wav2Vec2 transformer model on call center data for sentiment/tone analysis, deployed as a REST API microservice
Built a CatBoost-based claim denial prediction model achieving 92% Acc., deployed as a dockerized FastAPI service on AWS EC2

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Muhammad Z, M is an AI Engineer specializing in machine learning systems, data engineering, and applied AI solutions with hands-on experience building production-grade ML pipelines and intelligent automation systems. He works extensively with Python-based AI ecosystems to develop and deploy machine learning and deep learning models for real-world applications, including healthcare automation, speech processing, and computer vision. His expertise spans data engineering and analytics using SQL databases such as PostgreSQL,…
Set up & configured Ouster LiDAR & SDK on Raspberry Pi, achieving 35 FPS with 95% detection accuracy
LangChain-based conversational AI agent integrated with production SQL & scheduling APIs, reducing appointment booking time
Fine-tuned a Wav2Vec2 transformer model on call center data for sentiment/tone analysis, deployed as a REST API microservice
Built a CatBoost-based claim denial prediction model achieving 92% Acc., deployed as a dockerized FastAPI service on AWS EC2
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