Collaborate with data scientists and domain experts to operationalize experimental models, optimizing for performance, scalability, and latency to ensure high-quality datasets.
Implement and advocate for AI/MLOps practices (CI/CD for ML, model versioning, feature stores) using modern tools.
Optimize model inference for production environments (e.g., using TensorRT, ONNX, pruning, quantization).
Write robust, testable, and maintainable code in a collaborative setting using GitHub.
Integrate AI models into enterprise systems using APIs and cloud-native services (AWS, Azure).
Ensure models meet business objectives while adhering to ethical AI and governance frameworks.
Provide technical support and training to users.
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