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, 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.

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,

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).

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.