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AI Computer Vision Engineer

Industry: Software
Remote
Company size: 11-50
Full-time

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Job description

An AI-powered technology platform tailored for accurate predictive analytics and fault identification is looking for an AI Computer Vision Engineer. The engineer will be working with a talented, passionate, and hardworking team to build end-to-end customer-facing machine learning products and pipelines. The company has gained the support and partnership of top organizations like Google cloud, AWS, Microsoft Azure and focuses on safeguarding the world's energy infrastructure and disrupting the power utility industry. This role offers a unique opportunity to join a venture-backed and funded startup while contributing to scaling the vision of energy industry transformation. It is a full-time job that requires at least 5 hours overlap with PST.

 

Job Responsibilities:

  • Exploring complex algorithms for computer vision, image, and video processing 
  • Implement models that analyze faults, anomalies, segmentation, and classifications
  • Develop predictive models to generate predictive insights
  • Work on the deployment of dynamic models on cloud infrastructure, Kubernetes Engine, and as service REST APIs
  • Continuously implement ML models with the software platform
  • Integrate computer vision anomaly detection, classification, and segmentation models on multiple devices 

Job Requirements:

  • Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
  • At least 3 - 4+ years of relevant industry experience in building and deploying Computer Vision-based ML/AI models in production environments
  • Must be experienced in Python, PyTorch, Keras, Tensorflow, and Convolutional Neural Networks
  • Track record with 3D visual modeling, Point Clouds, and building Computer Vision models for the point clouds is a plus
  • Nice to have some experience in Mask RCNN, Geospatial data modeling, YOLO, Detectron 2, GCP, and Kubernetes
  • Sound expertise in processing 3D image point clouds 
  • Prior experience in building models on virtual machines, Google Kubernetes, and compute engines


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