Description

You will build and manage ML model operationalization pipelines within AWS GovCloud and Databricks environments.

This role is on-site.

Responsibilities

• Design and implement end-to-end MLOps architecture using Databricks Unity Catalog and MosaicAI.
• Operationalize ClassicAI and GenAI models in production environments.
• Develop distributed computing workflows using Spark and the Kubernetes ecosystem.
• Manage CI/CD DevOps processes for machine learning lifecycles.
• Collaborate with remote and global teams to deploy scalable Lakehouse monitoring solutions.

Required Skills

• 8-10 years of experience in data engineering, machine learning, or data science.
• 4-6 years of experience in AWS GovCloud, Export Control, or FedRAMP environments.
• 6-8 years of professional Python and PySpark coding experience.
• 4-6 years of experience with ML frameworks including Keras, TensorFlow, PyTorch, and HuggingFace Transformers.
• 4-6 years of experience with classification, clustering, optimization, deep learning, and NLP.
• 4-6 years of experience with Azure Databricks or AWS Databricks and serverless solutions.
• 4-6 years of experience with Spark and Kubernetes.
• 4-6 years of experience with CI/CD DevOps processes.

Preferred Skills

• Bachelor’s degree in Computer Science, Engineering, Applied Mathematics, or a related field.

Key Skills

Education

Bachelor's degree