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Machine Learning Engineer
4 weeks ago
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.