About the RoleWe are hiring a Machine Learning Engineer with a strong foundation in computer vision, imageclassification, image processing, and prompt-based generative modeling. In this role, you will focuson building and deploying production-grade ML pipelines that process images at scale, integrategenerative 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, anddiffusion-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, orControlNet).- 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, ONNXconversion, 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-basedtasks.- 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 orWeights & Biases).- Experience with model optimization tools (ONNX, TensorRT, quantization).- Comfortable working with GPU-based environments and optimizing training/inferenceperformance.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 scalabletraining/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.