Description

•   Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
•   Strong Experience in deployment/devops technologies: CI/CD pipelines, Kubernetes/Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.). and cloud-native architectures (GCP and Aruze), monitoring and observability for ML workloads
•   Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX.
Nice to have:
•   Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus.
•   Familiarity with model explainability, fairness, and bias detection tools is highly desirable.
•   Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance

Key Skills
Education

Any Gradute

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