Deep Learning Engineer (Computer Vision) (Hyderabad)

As a Computer Vision Engineer at our company, your role will involve researching, designing, and implementing advanced computer vision models for tasks such as image segmentation, pose estimation/transfer, and generative modeling. You will be responsible for developing efficient, scalable code in Python using PyTorch and other relevant libraries. Additionally, you will package and deploy ML models as APIs, preferably using FastAPI, for real-world usage. Your tasks will also include building lightweight frontend/backend dashboards or admin panels to monitor, visualize, and manage deployed models. Collaboration with cross-functional teams (product, data, engineering) to integrate AI solutions into products will be a key part of your responsibilities. Furthermore, you will need to optimize model performance for inference speed, scalability, and cost efficiency. Key Responsibilities: - Research, design, and implement advanced computer vision models for tasks such as image segmentation, pose estimation/transfer, and generative modeling. - Develop efficient, scalable code in Python using PyTorch (and other relevant libraries). - Package and deploy ML models as APIs (preferably using FastAPI) for real-world usage. - Build lightweight frontend/backend dashboards or admin panels to monitor, visualize, and manage deployed models. - Collaborate with cross-functional teams (product, data, engineering) to integrate AI solutions into products. - Optimize model performance for inference speed, scalability, and cost efficiency. Qualifications Required: - Solid hands-on expertise in Deep Learning and Computer Vision (e.g., segmentation, generative models, pose transfer). - Proficiency in Python and PyTorch for model development. - Experience with API development and model deployment (FastAPI, Flask, or similar). - Familiarity with frontend/backend development for building admin dashboards (React, Vue, Django, or similar). - Solid understanding of software engineering practices (Git, CI/CD, testing). - Experience with GPU-based training and optimization (CUDA, Torch Script, ONNX) is a plus. As a Computer Vision Engineer at our company, your role will involve researching, designing, and implementing advanced computer vision models for tasks such as image segmentation, pose estimation/transfer, and generative modeling. You will be responsible for developing efficient, scalable code in Python using PyTorch and other relevant libraries. Additionally, you will package and deploy ML models as APIs, preferably using FastAPI, for real-world usage. Your tasks will also include building lightweight frontend/backend dashboards or admin panels to monitor, visualize, and manage deployed models. Collaboration with cross-functional teams (product, data, engineering) to integrate AI solutions into products will be a key part of your responsibilities. Furthermore, you will need to optimize model performance for inference speed, scalability, and cost efficiency. Key Responsibilities: - Research, design, and implement advanced computer vision models for tasks such as image segmentation, pose estimation/transfer, and generative modeling. - Develop efficient, scalable code in Python using PyTorch (and other relevant libraries). - Package and deploy ML models as APIs (preferably using FastAPI) for real-world usage. - Build lightweight frontend/backend dashboards or admin panels to monitor, visualize, and manage deployed models. - Collaborate with cross-functional teams (product, data, engineering) to integrate AI solutions into products. - Optimize model performance for inference speed, scalability, and cost efficiency. Qualifications Required: - Solid hands-on expertise in Deep Learning and Computer Vision (e.g., segmentation, generative models, pose transfer). - Proficiency in Python and PyTorch for model development. - Experience with API development and model deployment (FastAPI, Flask, or similar). - Familiarity with frontend/backend development for building admin dashboards (React, Vue, Django, or similar). - Solid understanding of software engineering practices (Git, CI/CD, testing). - Experience with GPU-based training and optimization (CUDA, Torch Script, ONNX) is a plus.