Job Responsibilities
- Develop and train computer vision models for tasks such as image classification, object detection, and segmentation.
- Work with image datasets, including data cleaning, annotation support, preprocessing, and augmentation.
- Apply deep learning frameworks to build and improve model performance.
- Implement and test approaches for anomaly detection, including unsupervised defect localization using PatchCore, and work with up-to-date architectures such as Segment Anything (SAM) and Swin Transformers.
- Apply standard machine learning techniques such as Regression, Gradient Boosting, and Time-Series methods to integrate visual data with structured metadata for business insights.
- Evaluate model performance using metrics such as mAP, precision, recall, and F1-score, and follow established validation approaches to ensure model reliability.
- Assist in integrating models into existing pipelines and support deployment efforts.
- Work closely with cross-functional teams to communicate results to technical stakeholders and clients.

Technical Skills & Stack




- Computer Vision & Deep Learning (Core Focus): Libraries: PyTorch, OpenCV, scikit-image, Detectron2, MMSegmentation, and anomalib.
- Architectures: ResNet, Faster R-CNN, Swin Transformer, Autoencoders (SAE/VAE), and Masked Autoencoders (MAE).
- Concepts: Image classification, object detection, segmentation, basic CNN architectures Machine Learning & Data Science: Techniques: Supervised/Unsupervised learning, XGBoost, LightGBM, Random Forest, PCA/SVD (Dimensionality Reduction), and Cross-Validation.
- Libraries: Scikit-learn, XGBoost, Pandas, NumPy, SciPy, and Matplotlib.
- Data & Tools: Basic SQL knowledge Familiarity with Git or version control Experience working with structured and unstructured datasets

Education & Experience Experience:
- 4-5 years of overall experience, with recent hands-on work in Computer Vision projects for at least 1 year
- Education: Bachelor's degree in Computer Science, En

📌 ML Engineer (Hyderabad)
🏢 Soothsayer Analytics
📍 Hyderabad