Machine Learning Engineer

1 week ago


Gurugram, India Shiprocket Full time

About the Role We are hiring a Machine Learning Engineer with a strong foundation in computer vision, image classification, image processing, and prompt-based generative modeling. In this role, you will focus on building and deploying production-grade ML pipelines that process images at scale, integrate generative models, and power visual AI products. Responsibilities - Build and optimize ML pipelines for image classification, detection, and segmentation tasks. - Design, train, fine-tune, and deploy deep learning models using CNNs, Vision Transformers, and diffusion-based models. - Work with image datasets (structured/unstructured), including preprocessing, augmentation, normalization, and enhancement techniques. - Implement and integrate prompt-based generative models (e.g., Stable Diffusion, DALLE, or ControlNet). - Collaborate with backend and product teams to deploy real-time or batch inference systems (using Docker, TorchServe, TensorRT, etc.). - Optimize model performance for speed, accuracy, and size (quantization, pruning, ONNX conversion, etc.). - Ensure robust versioning, reproducibility, and monitoring of models in production. Required Skills - 2-4 years of experience building and deploying ML models in production environments. - Strong proficiency in Python and deep learning frameworks like PyTorch or TensorFlow. - Hands-on experience with CNNs, ViTs, UNets, or other architectures relevant to image-based tasks. - Experience with prompt-based image generation models (e.g., Stable Diffusion, Midjourney APIs, DALLE, or open-source alternatives). - Familiarity with OpenCV, albumentations, or similar libraries for image processing. - Ability to train and evaluate models on large datasets with proper tracking (e.g., using MLflow or Weights & Biases). - Experience with model optimization tools (ONNX, TensorRT, quantization). - Comfortable working with GPU-based environments and optimizing training/inference performance. Nice to Have - Experience with ControlNet, LoRA, or DreamBooth for custom generative image tuning. - Familiarity with deployment using TorchServe, FastAPI, or Triton Inference Server. - Knowledge of cloud infrastructure (e.g., AWS Sagemaker, GCP AI Platform) for scalable training/inference. - Basic understanding of CI/CD pipelines for ML (MLOps practices). What We Offer - Opportunity to work on cutting-edge generative and visual AI problems. - Collaborative and engineering-driven culture. - Access to high-performance GPUs and scalable compute resources.



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