Job Description:

Pay Range: $55hr - $60hr

Responsibilities:

Design and implement CI/CD pipelines for ML workflows using Databricks and other tools.
utomate model training, validation, deployment, and monitoring processes.
Collaborate with data scientists to productionize ML models and ensure reproducibility.
Manage and optimize Databricks clusters, jobs, and workflows.
Implement model versioning, experiment tracking, and performance monitoring.
Ensure compliance with data governance, security, and privacy standards.
Troubleshoot and resolve issues in ML pipelines and infrastructure.
Contribute to the development of internal MLOps best practices and documentation.

Required Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
8+ years of experience with strong MLOps skills or with ML engineering roles
Strong experience with Databricks, including notebooks, jobs, Delta Lake, and MLflow.
Proficiency in Python and familiarity with ML libraries ( TensorFlow, PyTorch).
Experience with Azure cloud platform.
Familiarity with containerization (Docker) and orchestration (Kubernetes) is a plus.
Experience with CI/CD tools (e.g., GitHub Actions, Jenkins, Azure DevOps).
Strong understanding of data pipelines, ETL processes, and data engineering principles.
Databricks certification (e.g., Databricks Certified Machine Learning Professional).
Experience with feature stores, model registries, and monitoring tools.
Knowledge of Spark and distributed computing concepts.
Familiarity with infrastructure-as-code tools (e.g., Terraform).

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