ML Engineer

2 weeks ago

Gurgaon, Haryana, India Shiprocket Full time ₹ 15,00,000 - ₹ 20,00,000 per year

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).

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