Computer Vision Engineer
Detalhes do emprego
Designation: Computer Vision EngineerLocation: GurgaonSalary: Upto 26 LPAWorking: 5 DaysExperience: 4+ yearsRole and Responsibilities:- Develop, train, test, and deploy machine learning models using Pytorch / Tensorflow.- Building Models for tasks like NLP, STT, and TTS.- Proficiency in Image Processing - enhancement, compression, restoration, filtering, and manipulation techniques.- Knowledge of feature extraction methods and image segmentation.- Implement and maintain machine learning pipelines to support data ingestion, processing, and transformation.- Building and training convolutional neural networks (CNNs) for image recognition tasks.- Strong understanding of AI fundamentals: The candidate should have a deep understanding of the fundamentals of AI, including machine learning, natural language processing, computer vision, expert systems, and other techniques.- Programming skills: The candidate should be proficient in at least one programming language, such as Python, Java, C++, or R.- Knowledge of cloud-based AI platforms: Many companies are moving towards cloud-based AI platforms such as AWS, Azure, and Google Cloud. The candidate should have experience with at least one of these platforms and be comfortable working with cloud-based services.- Data analysis and data engineering: The candidate should be skilled in data analysis and data engineering techniques, which are essential for building effective AI models.- Stay up-to-date with the latest trends and developments in machine learning, deep learning, and artificial intelligence.Skills Required:- Minimum of 4 years of experience as a Machine Learning Engineer or a similar role.- Strong programming skills in Python and experience with Pytorch and TensorFlow.- Experience with data pre-processing, feature engineering, and data analysis.- Should have worked extensively on Deep Neural Network- Should have experience of Models like GAN, Conditional GAN, Transformers etc- Should have used/exposed with Stable Diffusion , Latent Diffusion, InPainting, Text-to-Image Model, Image-to-Image Model etc- Strong understanding of machine learning algorithms and techniques, such as deep learning, supervised and unsupervised learning, reinforcement learning, and natural language processing. PRB
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