MLOps Engineer
Indexed description
Requirements
Programming & Development:
- Advanced proficiency in Python development
- Working experience with PyTorch and/or TensorFlow
- Ability to create maintainable, well-tested code with proper error handling
- Experience creating RESTful services and model serving endpoints
- Design and implementation of automated ML workflows (training, evaluation, deployment)
- Experience with model versioning, storage, and metadata management
- Implementing dataset versioning and change management
- Knowledge of model optimization and format conversion (ONNX, TFLite, TensorRT, SNPE)
- Docker image building and optimization
- Experience with Kubernetes and/or RunAI
- GitHub Actions workflow design and implementation
- GCP experience, particularly with Vertex AI, Cloud Batch, or similar services
- GPU allocation and optimization for training workloads
- Unit, integration, and E2E test implementation for ML systems
- Model metrics calculation and performance benchmarking
- Experience with testing ML models on edge devices
- Experience with Hydra or similar configuration frameworks
- Familiarity with W&B, MLflow, or similar tracking tools
- Creating and maintaining Python packages and dependencies
- Understanding of CV models, image processing, and related ML tasks
- Design and implement an end-to-end MLOps architecture for computer vision models
- Create automated pipelines for model training, quantization, conversion, and evaluation
- Develop a model registry with comprehensive versioning and metadata capabilities
- Build systems for automated model testing across multiple target architectures
- Implement data versioning and dataset management solutions
- Maintain CI/CD pipelines for ML model lifecycle management
- Optimize Docker environments for development and deployment
- Develop Python libraries and APIs for internal model consumption
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