Fariha Muazzam
I build production-ready AI systems that actually ship
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Satellite imagery is the ultimate stress test for Computer Vision.
It is not just about detection; it is about architecting precision across massive, high-resolution datasets where every pixel counts.
I have been building AI pipeline to segment parking lots, walkways, and structures from aerial data. The challenge is not just the mode, it is the Infrastructure:
- Tiling & Inference: Processing massive dimensions without losing small-object context.
- Robust Pre-processing: Using OpenCV to handle varying lighting and atmospheric "noise."
- Cloud Orchestration: Turning heavy segmentation models into responsive, scale-ready tools.
It is about turning raw pixels into a reliable data layer for the business.
Currently opening 20 hours/week for long-term partnerships in Computer Vision & AI Systems.
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AI-based Video Generation Pipeline
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I designed and delivered an end-to-end MVP for a real-time AI talking avatar system, where live browser speech is converted into an AI-generated response and rendered through a lip-synced avatar video on GPU hardware.
The objective was to validate technical feasibility, latency characteristics, and perceived real-time interaction before committing to production hardening. The system integrates speech-to-text, LLM-based reasoning, text-to-speech, and video synthesis into a single, runnable pipeline, deployed on an A100 GPU.
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At Formulatrix, I led the development of a computer vision pipeline for RockMaker, a biotech product used in crystallization experiments. I built and deployed object detection and image classification models that improved scoring accuracy by 13%, reducing manual effort for scientists and increasing customer satisfaction. The solution was productionized with Docker and CI/CD pipelines, ensuring scalability and reliability across client sites.
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