Key accomplishments

  • Highly skilled computer vision and deep learning research engineer with 6+ years of experience in developing end-to-
    end cutting-edge computer vision products using frameworks such as TensorFlow, PyTorch, TensorRT, ONNX and MLOps
    tools (e.g. Kubernetes, Prometheus, Grafana, and Docker products).
  • Specialized in developing efficient microservices architecture for real-time object detection, retrieval, and tracking tasks
    from inception to production on edge compute (e.g. NVIDIA GPUs, Nano, Edge TPU) and cloud (e.g. GCP, Azure) for
    efficient and scalable production systems.
  • Passionate about exploring full AI ecosystem, from training Vision Transformers (ViT) to designing inference architecture
    for production, with a focus on solving real-world problems and creating value for customers and businesses.
Role 1
Computer vision engineer
Role 2
Machine learning engineer
B2b software and services > engineering product and design
part-time-16-hours-per-weekfull-time 40-hours-per-weekcontract
Any
Senior
Canada
Any
NA
Remote
Willing to work for equity + rewardsWilling to work for paid projectsWilling to work for stock and paid projects

Tags

AITechRetailtechHealthtech

Education

Experience

Portfolio

Expertise

Deep Learning
90
Docker
90
Kubernetes
80
Python
95
Prometheus
85
Grafana
90

Languages

English

Honors & awards

  • 2017

    NSERC Research Grant

    As a research assistant at Dalhousie University, my work focused on studying classical computer vision algorithms (SIFT, SURF, HOG, Hough Transform) and state-of-the-art deep learning techniques (Fast R-CNN, Faster R-CNN) for sea scallop recognition and detection in high-resolution underwater RGB images, as well as non-uniform illumination correction using surface fitting techniques.

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