
MLOps Expert in Pharmaceutical Domain
3 days ago
Overview of the Role:
- This is a position that supports model deployment, orchestration, and operational processes within a pharmaceutical domain environment.
- The ideal candidate will have a strong background in software development, data engineering, and machine learning.
MLOps Engineering Responsibilities:
Deployment and Orchestration- Support the deployment of models using AWS and Databricks.
- Implement robust ML pipelines and production readiness rather than developing new models.
- Deploy R-based models using AWS and Databricks.
- Implement data and model versioning strategies to ensure reproducibility and reliability.
- Manage CI/CD pipelines using GitHub for seamless integration and deployment.
- Use MLflow or equivalent tools to efficiently deploy and monitor models.
- Troubleshoot issues in R/Python-based implementations to minimize downtime and ensure optimal performance.
- Draft Standard Operating Procedures (SOPs) to ensure smooth operation and compliance.
Requirements:
- 5 years of experience in MLOps, software development, or related roles.
- Proven expertise with MLOps tools, CI/CD, and monitoring solutions.
- Strong Python programming skills; experience with R and SAS is a plus.
- Knowledge of package management, dependency tracking, and version control.
- Solid understanding of data engineering, ETL processes, and data modeling.
- Familiarity with big data technologies.
- Experience with cloud automation and orchestration preferably on AWS.
- Understanding of data science concepts, machine learning algorithms, RESTful APIs, JSON, and other data exchange formats.
- Pharma domain experience required.
This role focuses on enabling robust ML pipelines and production readiness. The ideal candidate should be passionate about MLOps engineering and have a strong background in software development and data engineering.
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