Job Description
Requirements:
Must have:
- 4–6 years of experience in Machine Learning Engineering with strong expertise in MLOps and ML system architecture.
- Advanced proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
- Hands-on experience with MLOps platforms and orchestration tools, including MLflow, Kubeflow, AWS SageMaker, Airflow, or similar.
- Strong knowledge of cloud platforms (preferably AWS) and container technologies (Docker, Kubernetes).
- Experience implementing model monitoring, logging, performance optimization and inference pipelines.
- Solid understanding of data pipelines, APIs, distributed systems, and real-time/ batch data processing.
- Experience with AWS Data Pipeline and cloud-native ML services.
- Strong ability to work cross-functionally with Data Scientists, Engineers, and business stakeholders.
- Preferred certifications: AWS Machine Learning Specialty, Google Profes...
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