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Strong employer brand and remote option balanced by senior, specialized MLOps requirements.
High domain bias due to regulated clinical MLOps and Azure Databricks specialization.
Explicit 9-10 years and mandatory Azure Databricks/MLflow/MLOps expertise.
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Design, build, and operationalize production-grade ML and GenAI/LLM solutions for Clinical Operations on Azure Databricks (including MLflow, Unity Catalog).
Own end-to-end ML lifecycle: data ingestion, feature engineering, versioning, serving, and monitored production deployment in regulated environments.
Establish and uphold MLOps best practices including CI/CD pipelines, real-time/batch model serving, monitoring, and compliance with GxP and data governance standards.
9-10 years of hands-on ML engineering or MLOps experience deploying and operating ML models in enterprise production.
Bachelor’s or master’s degree in Computer Science, Software Engineering, Mathematics, Statistics, or related quantitative field.
Strong expertise in Azure Databricks ecosystem, MLflow, Python programming, ML frameworks (Scikit-learn, PyTorch, TensorFlow), API development for scalable ML microservices, and CI/CD with Azure DevOps or GitHub Actions.
Experience with containerization (Docker), Azure cloud services (ADLS Gen2, Key Vault, Azure Monitor), and compliance with regulated environments (GxP).
Experienced in architecting and delivering scalable, maintainable ML systems with rigorous engineering discipline in regulated, enterprise environments.
Comfortable driving technical leadership and collaborating across cross-functional agile teams including Data Scientists, Data Engineers, and Clinical stakeholders.
Strong focus on practical, production-ready engineering solutions, compliance with data governance and responsible AI principles, and mentoring junior team members.