Computer Vision & Machine Learning Engineer
Tasks
- Adapt implement algorithms from research papers
- Build deep learning and generative AI models
- Build model serving pipelines with latency and throughput targets
- Conduct code reviews and write integration tests for ML pipelines
- Conduct structured error analysis and failure mode categorization
- Design data pipelines for ingestion preprocessing annotation and quality monitoring
- Develop production grade Python libraries for the ML lifecycle
- Own end to end computer vision projects
- Perform experiment tracking and model versioning
- Perform systematic hyperparameter tuning and ablation studies
- Scope plan and execute projects from problem framing to deployment
- Serve deployed models and monitor performance
- Translate client requirements and raw data into computer vision solutions
- Validate models against baselines and benchmark for production
Perks/Benefits
- N/A
Skills/Tech-stack
Ablation Studies | CI/CD | Computer Vision | Data Pipelines | Docker | Error Analysis | Experiment tracking | FastAPI | Foundation Models | Git | GitHub Actions | Hyperparameter Tuning | Image classification | Language Models | Latency optimization | Model Quantization | Model Serving | Model versioning | Multimodal Learning | NumPy | Object Detection | OpenCV | Pandas | PyTorch | PyTorch Lightning | Pydantic | Pytest | Python | Reproducible environments | Scikit-learn | Semantic Segmentation | Similarity Search | Vision Language Models | Vision Transformers | Vision-language
Education
N/A
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