Data Scientist
Skills
About the role
This is a hybrid Data Scientist role in Bangalore for someone with 3–8 years of experience developing computer vision and deep learning systems. You'll build models for image and video use cases, manage data and experimentation, and deploy production AI with MLOps practices.
What you’ll do
- Design and optimize computer vision machine learning solutions.
- Build and fine-tune models for classification, detection, and segmentation.
- Process, annotate, and manage large image and video datasets.
- Create preprocessing and post-processing pipelines.
- Train, evaluate, and validate deep learning models.
- Monitor model performance and conduct error analysis.
- Benchmark experiments and compare models.
- Deploy and scale AI models in production.
- Work with engineering, product, and business teams on integration.
- Maintain version control, testing, documentation, and reproducibility.
What they’re looking for
- Three to eight years of experience.
- Python and computer vision experience.
- Deep learning with PyTorch, TensorFlow, or Keras.
- OpenCV and image-processing experience.
- Experience with classification, object detection, and image segmentation.
- Model evaluation, fine-tuning, optimization, and deployment experience.
- Git and software development best practices.
- MLOps and production model deployment experience.
Nice to have
- Vision-language models and multimodal AI.
- Large language models and NLP.
- MLflow and Kubeflow.
- Docker and Kubernetes.
- AWS, Azure, or GCP.
- Healthcare AI or medical imaging.
- Responsible AI and AI ethics.
- Research publications or open-source AI contributions.
What’s on offer
- Hybrid work in Bangalore.
- The role is with Fractal.
Questions about this role
Where is this Data Scientist role based?
The role is based in Bangalore and is hybrid.
How much experience is required?
The posting asks for 3–8 years of experience.
What computer vision work is involved?
The role covers image classification, object detection, image segmentation, image and video data, and model optimization.
Which MLOps tools are mentioned?
MLflow, Kubeflow, Docker, and Kubernetes are listed.
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