Senior ML Ops Engineer

Paradigm; Irving (TX); On-site; USD 140,000 - 200,000; Full time

Job summary

Paradigm is hiring a Senior ML Ops Engineer to design, build, and maintain scalable ML deployment pipelines that support the entire lifecycle from training to retraining. You will deploy models to production, ensure reliability and security, and implement governance with model registries and experiment tracking.

You will collaborate with ML engineers, data scientists, software engineers, and infrastructure teams to operationalize ML solutions.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, AI 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.

Responsibilities

• 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.

Skills

MLOps experience

Python programming

Cloud ML experience

Analytical thinking

Team collaboration

Education

Bachelor’s degree in Computer Science or related field

Tools

MLflow

Kubeflow

Azure Machine Learning

Databricks

Docker

Kubernetes

Terraform

Job description

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 myparadigm.com/careers/