About the Role
- 4–6 years of experience in ML engineering , with strong expertise in MLOps and system architecture .
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and Scikit-learn .
- Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow).
- Strong knowledge of cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
- Experience with model monitoring, logging, and performance optimization .
- Solid understanding of data pipelines, APIs, and distributed systems .
- Preferred certifications: AWS Machine Learning Specialty, Google Professional ML Engineer, or equivalent.
- Advanced English proficiency , able to document and present technical solutions to diverse stakeholders.
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