ML OPS - Lead

Job Description

• 8+ years of experience in ML Ops engineering or a related field.
• Strong proficiency in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, scikit-learn, etc.
• Extensive experience with ML Ops frameworks like Kubeflow, MLflow, TensorFlow Extended (TFX), KubeFlow Pipelines, or similar.
• Strong experience in deploying and managing machine learning models in cloud environments (AWS, GCP, Azure).
• Proficiency in managing CI/CD pipelines for ML workflows using tools such as Jenkins, GitLab, CircleCI, etc.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) technologies for model deployment.
  • Skills : SageMaker, PySpark, AWS Services ; Nice to have:  MLOps