Saher Sajid, Developer in Montreal, QC, Canada
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Saher Sajid

Verified Expert  in Engineering

Machine Learning Engineer and Software Developer

Location
Montreal, QC, Canada
Toptal Member Since
July 18, 2023

Saher is an experienced computer vision engineer with an extensive ability to synergize traditional machine learning methods and contemporary approaches. Excelling in object detection, recognition, and visual data synthesis, she has an impressive track record of thriving solutions: creating an innovative speed estimator using stereo vision at Hazen.ai and contributing to Saudi Arabia's e-governance program at Addo.ai. Saher aspires to improve model accuracy and reduce training time continuously.

Portfolio

Hazen.ai
Python, PyTorch, OpenCV, C#, Artificial Intelligence (AI), Docker
Addo.ai
Python, Artificial Intelligence (AI)

Experience

Availability

Part-time

Preferred Environment

Deep Learning, Linux, PyTorch, OpenCV, Python, C#

The most amazing...

...product I've built is a speed estimator using stereo vision.

Work Experience

Senior Machine Learning Engineer

2020 - 2023
Hazen.ai
  • Developed a vehicle instantaneous speed detection system estimating speed within 10% of the ground truth in the field. The proof of concept employed existing deep neural networks for feature finding and matching to estimate disparity.
  • Fine‑tuned a pruned version of SqueezeDet, a fully convolutional deep neural network for real-time object detection for custom classes.
  • Trained autoencoder-based neural networks for Optical Character Recognition, Automatic License Plate Recognition (OCR/ALPR). The final model was deployed on edge devices for real-time detection with accuracy upwards of 95% on clean plates.
  • Built a synthetic data generation pipeline using Unity 3D's Perception toolkit to provide annotated data for training models such as pose estimation and object detection.
  • Ported OpenCV's pattern detection function to Python to develop a camera calibration process that could work within defined constraints.
Technologies: Python, PyTorch, OpenCV, C#, Artificial Intelligence (AI), Docker

Machine Learning Engineer

2019 - 2020
Addo.ai
  • Consulted on Saudi Arabia's e-governance program and proposed AI use cases addressing clients' pain points identified during discovery sessions.
  • Provided consultation to a travel company to improve lead conversion rates. The project focused on developing a strategy enabling call center agents to prioritize their efforts on leads more likely to convert into customers.
  • Designed the migration of a revenue assurance platform for Telenor, a telecom company. This was part of the data platform solution for Telenor's switch to big data platforms from legacy DBMS systems like Teradata.
Technologies: Python, Artificial Intelligence (AI)

Vehicle Instantaneous Speed Using Stereo Vision

The development of an innovative system designed to detect a vehicle's instantaneous speed using a vertically aligned stereo system, a novel approach in the field that I spearheaded.

The project's initial phase involved calibrating the stereo system, which I accomplished using OpenCV. To ensure the accuracy of the calibration parameters, I utilized the MATLAB calibration tool as a secondary verification method.

The core of the system's functionality lies in its use of deep neural networks for feature finding and matching. These networks implemented using PyTorch were integral in estimating depth, a critical factor in determining vehicle speed.

Languages

Python, C++, C#, Embedded C++

Libraries/APIs

PyTorch, OpenCV

Other

Deep Learning, Machine Learning, Computer Engineering, Artificial Intelligence (AI), Game AI, Natural Language Processing (NLP), Embedded Systems

Frameworks

Unity3D

Platforms

Linux, Docker

2018 - 2019

Master's Degree in Artificial Intelligence

Queen Mary, University of London - London, United Kingdom

2011 - 2015

Bachelor's Degree in Computer Engineering

College of Electrical and Mechanical Engineering, NUST - Rawalpindi, Pakistan

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