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Tier-1 employer, mid-level MLOps role in metro with a 4+ year requirement increases competition.
Pharmaceutical model lifecycle and scientific collaboration require domain experience, reducing cross-industry transferability.
Mandatory 4+ years, specific MLOps stack, and pharmaceutical validation requirements make screening highly strict.
Drive the model lifecycle sustainability efforts for machine learning and data-driven models within Pharmaceutical Product Development, ensuring reproducibility, observability, governance, and value after deployment.
Develop and implement MLOps frameworks, deployment, monitoring, and continuous improvement workflows for advanced models across manufacturing, product performance, and analytics.
Build scalable model discoverability, governance standards, and monitoring strategies including alerting for model performance, data drift, and infrastructure health to support model reuse across programs.
Bachelor's degree in Computer Science, Engineering, Statistics, Data Science or related discipline.
At least 4 years industry experience with MLOps including Git, CI/CD, automated testing, model registries, experiment tracking, observability, versioning, and governance.
Hands-on experience with Databricks, AWS, MLflow, Docker, Kubernetes, GitHub Actions, Azure DevOps, Lakehouse Monitoring, Evidently AI.
Strong expertise in Python, PySpark, and knowledge of machine learning libraries such as scikit-learn and PyTorch.
Experienced in deploying, operating, monitoring, and maintaining production ML solutions through multiple lifecycle stages in regulated, scientific environments.
Able to translate complex scientific and technical needs into scalable platform capabilities and adoption roadmaps, balancing rigor, engineering quality, and practical business outcomes.
Skilled at engaging diverse stakeholders across scientific, engineering, product, and governance teams and driving alignment on technical standards and lifecycle practices.