Senior ML Ops Engineer

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior ML Ops Engineer to be part of revolutionizing these industries. We are building the future with modern software engineering, agent-assisted systems, and mobile-first experiences. We are powered by our parent company, Builders FirstSource (NYSE: BLDR): a Fortune 300 company with over $23 billion in revenue and more than 29,000 employees across 550+ locations, BFS is redefining construction through data, digital infrastructure, and AI-powered innovation.

What You Will Do:

· Design, build, and maintain scalable MLOps solutions that support the end-to-end machine learning lifecycle, including model training, deployment, monitoring, and retraining.

· Develop and optimize automated ML deployment pipelines, ensuring reliable, reproducible, and efficient model delivery to production environments.

· Deploy and support machine learning models and AI solutions in production, maintaining best practices for scalability, reliability, security, and operational excellence.

· Implement and maintain model registries, experiment tracking, versioning, and governance practices to support consistent model lifecycle management.

· Build and support containerized ML workloads and deployment workflows using technologies such as Docker and Kubernetes.

· Develop monitoring, observability, and alerting capabilities for machine learning systems, including model performance tracking, drift detection, and data quality monitoring.

· Collaborate with Machine Learning Engineers, Data Scientists, Software Engineers, and Infrastructure teams to operationalize ML solutions and improve deployment efficiency.

· Implement and maintain IaC patterns using Terraform.

· Troubleshoot and resolve complex technical challenges related to model deployment, ML infrastructure, and production operations.

· Provide guidance and mentorship to other engineers.

What You Need to Succeed:

· Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence or related field or equivalent experience.

· 4+ years of professional experience in software engineering, machine learning engineering, MLOps, platform engineering, DevOps, or a related technical discipline.

· Strong understanding of the machine learning lifecycle, including model training, validation, deployment, monitoring, and retraining.

· Experience building and maintaining automated machine learning pipelines and CI/CD workflows.

· Experience with MLOps platforms and tools such as MLflow, Kubeflow, Azure Machine Learning, Databricks, or similar technologies.

· Experience in Python programming, ML Framework and Agentic AI. Implemented model monitoring, experiment tracking, model versioning, and governance practices.

· Experience working with cloud-based machine learning solutions, preferably within Azure.

· Ability to independently solve complex technical challenges, make sound decisions with minimal guidance, and drive work to completion.

Ready to Join? Apply now at