ML Engineer

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.

Role & 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, DALL•E, 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.

Preferred candidate profile

• 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,
• DALL•E, 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).