Key Responsibilities:
Design and implement end-to-end ML pipelines using tools like MLflow, KubeFlow, DVC, and Airflow.
Develop and maintain CI/CD workflows using Jenkins, CircleCI, Bamboo, or DataKitchen.
Containerize applications using Docker and orchestrate them with Kubernetes or OpenShift.
Collaborate with data scientists to productionize ML models and ensure reproducibility and scalability.
Manage data versioning and lineage using Pachyderm or DVC.
Monitor model performance and system health using Grafana and other observability tools.
Write robust and maintainable code in Python, Go, R, or Julia.
Automate workflows and scripting using Shell Scripts.
Integrate with cloud storage solutions like Amazon S3 and manage data in RDBMS such as Oracle, SQL Server, or open-source alternatives.
Ensure compliance with data governance and security best practices.
Required Skills and Qualifications:
Strong programming skills in Python and familiarity with ML/DL libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience with MLOps tools such as MLflow, KubeFlow, DVC, Airflow, or Pachyderm.
Proficiency in Docker and container orchestration using Kubernetes or OpenShift.
Experience with CI/CD tools like Jenkins, CircleCI, Bamboo, or DataKitchen.
Familiarity with cloud storage and data management practices.
Knowledge of SQL and experience with RDBMS (Oracle, SQL Server, or open-source).
Experience with monitoring tools like Grafana.
Strong understanding of DevOps and software engineering best practices.
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