Computer Vision Engineer

Computer Vision Engineer

IT-Workz | Thiruvananthapuram, KL, IN

Posted a month ago

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Experience: 0 to 3 Years

Key Responsibilities:
Research, develop, evaluate and optimize various computer vision and deep learning models for different problems statements.
Take ownership, explore and analyse unstructured data like images through image/video processing.
Deploying developed computer vision models on edge devices after optimization to meet the product requirements and maintain them to later improve to address additional requirements in future.
BTech, MSc or MCA in Computer Science, Data Science, Machine Learning or in related fields preferred with a strong technical knowledge and zeal in computer vision.
Understanding about depth and breadth of computer vision and deep learning algorithms.
Experience with any machine/deep learning frameworks like Tensorflow, Keras and PyTorch.
Experience in training models.
Experience in using both basic and advanced image processing algorithms for feature engineering.
Proficiency in Python and related packages like numpy, scikit-image, PIL, opencv, matplotlib, seaborn, etc.
Excellent written and verbal communication skills for effectively communicating with the team and ability to presenting information to varied technical and non-technical audience.
Must be able to produce solutions independently in an organized manner and also be able to work in a team when required.
Must have good Object-Oriented Programing & logical analysis skills in Python.
Experience in DeepLab or similar models.
Must have curiosity, eagerness and motivation to be involved in Data Science and Image Processing.
Strong foundation in data structures and algorithms in Python or C++
Advanced knowledge in performance, scalability, numerical accuracy and best practices for implementing various solutions.
Exposure to AWS or Azure cloud computing environments.
Exposure to IoT technology.
Knowledge in Agile Application Development and Scrum methodologies to develop efficient, maintainable, readable and production-ready pipelines.