Data Engineering Professional II
Core
Build and maintain MLOps platforms and pipelines to operationalize machine learning and generative AI models for clinical development, regulatory operations, and translational research in a GxP-compliant environment.
Role type
Senior MLOps Engineer (Data Engineering focus)
Builds
Production-grade, auditable ML services and data pipelines on AWS and Databricks
Domain
Life Sciences / Pharma R&D / Regulated Systems
Deliverable
production ML models
Required skills
MLOps, Databricks (Delta Lake, MLflow, Unity Catalog), AWS (SageMaker, Bedrock, IAM, Lambda), Python, SQL, CI/CD, Infrastructure as Code (Terraform), Containerization (Docker/Kubernetes), ML lifecycle management
Preferred skills
GxP/CSV/CSA compliance, Pharma data domains (clinical trials, pharmacovigilance), GenAI/LLM operationalization, Streaming data (Kafka/Kinesis), Data observability
Technologies
Databricks, AWS, Terraform, Docker, Kubernetes, SageMaker, Bedrock, MLflow, Unity Catalog, Kafka, Kinesis
Responsibilities
Design and operate end-to-end ML pipelines from ingestion to monitoring; Implement CI/CD for ML assets and automated testing; Build and manage secure ML infrastructure on AWS using IaC; Implement model and data monitoring with drift detection and alerting; Build systems meeting GxP, 21 CFR Part 11, and ALCOA+ requirements; Partner with data scientists to productionize models and translate research code into robust services
Seniority
Senior, hands-on IC
