Data Engineering Professional II
Core
Build and maintain MLOps platforms and pipelines to operationalize machine learning and generative AI models from experiments into validated, production-grade, GxP-compliant services for clinical development and regulatory operations.
Role type
Senior MLOps Engineer (Data Engineering)
Builds
End-to-end ML pipelines, model registries, and cloud infrastructure on AWS and Databricks
Domain
Life Sciences / Pharma R&D / Regulated Systems
Deliverable
production ML models
Required skills
Python, SQL, Databricks (Delta Lake, MLflow, Unity Catalog), AWS (SageMaker, Bedrock, IAM, Lambda, ECS/EKS), CI/CD, Terraform, Docker, Kubernetes, Spark
Preferred skills
GxP/CSV/CSA compliance, LLM/GenAI operationalization, Kafka/Kinesis, data observability
Technologies
Databricks, AWS, Terraform, MLflow, Unity Catalog, SageMaker, Bedrock, Kubernetes, Docker, GitHub Actions, GitLab CI, Jenkins
Responsibilities
Design and operate end-to-end ML pipelines; build and manage ML infrastructure on AWS; implement model and data monitoring; build ML systems meeting GxP and regulatory requirements; partner with data scientists to productionize models
Seniority
Senior, hands-on IC