Senior ML Ops Engineer — Scale Deployments & AI Pipelines

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 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.