Machine Learning Engineer- REMOTE

This role is for one of the Weekday's clients Min Experience: 3 years Location: Remote (India) JobType: full-time This role is ideal for a machine learning professional who is passionate about solving complex visual intelligence problems using cutting-edge deep learning techniques.
You will work on designing and deploying image-focused ML solutions that power real-world applications, from visual understanding to image generation.
Collaborating closely with engineering, product, and research teams, you will help translate business and product challenges into scalable, production-ready computer vision systems while continuously pushing the boundaries of model performance and reliability.
Key Responsibilities Design, develop, and deploy machine learning models for image-based tasks such as classification, object detection, segmentation, super-resolution, and image generation Partner with cross-functional teams to define imaging use cases, translate requirements into technical solutions, and deliver end-to-end ML workflows Build and maintain robust image preprocessing, augmentation, and data validation pipelines for diverse datasets Implement, train, and fine-tune deep learning architectures including CNNs, vision transformers, diffusion models, and other modern vision frameworks Evaluate model performance using quantitative metrics and visual analysis to identify failure modes and improvement opportunities Optimize models for scalability, latency, and real-time inference through techniques such as quantization, pruning, and efficient architecture design Contribute to production-grade ML pipelines, including model versioning, deployment, monitoring, and MLOps best practices Stay current with the latest research in computer vision and apply innovative approaches to solve business-critical challenges What Makes You a Great Fit 3+ years of hands-on industry experience in machine learning and deep learning with a strong focus on computer vision Solid understanding of core vision concepts such as convolutional networks, feature extraction, image transformations, and geometric reasoning Strong proficiency in Python and deep learning frameworks including PyTorch or TensorFlow, along with tools like OpenCV and scikit-learn Experience training and tuning large-scale models on GPU-based infrastructure Strong grasp of model evaluation techniques and image quality metrics such as IoU, PSNR, and SSIM Hands-on exposure to deploying ML models in production environments using Docker and modern MLOps practices A curious, research-driven mindset with the ability to translate new ideas into practical, high-impact solutions Bonus experience with transformer-based vision models, multimodal learning, synthetic data generation, or edge/embedded vision systems