Senior MLOps Engineer: Scale AI for Construction

Energy Jobline ZR; Denver (CO); On-site; USD 123,000 - 170,000; Full time

Job summary

Paradigm, a software company transforming the residential, construction and building product industries, seeks a Senior ML Ops Engineer to join the team in Denver. You will design, build, and maintain scalable MLOps solutions spanning the end-to-end ML lifecycle, including training, deployment, monitoring, and retraining, with a focus on Azure-based workloads.

You will implement model registries, experiment tracking, governance, and CI/CD pipelines, containerize workloads with Docker/Kubernetes,

Qualifications

• 4+ years of professional experience in software or ML engineering, MLOps, or related field.
• Experience with automated ML pipelines and CI/CD workflows.
• Experience with cloud-based ML solutions, preferably Azure.
• Experience with MLOps platforms like MLflow, Kubeflow, Azure ML, Databricks.

Responsibilities

• Design, build, and maintain scalable MLOps solutions across the ML lifecycle.
• Develop and optimize automated ML deployment pipelines.
• Deploy and support ML models in production with best practices for reliability and security.
• Implement model registries, experiment tracking, and governance.
• Build containerized ML workloads with Docker and Kubernetes.
• Develop monitoring and observability for ML systems.
• Collaborate with ML engineers, data scientists, software, and infra teams.
• Implement IaC with Terraform.
• Troubleshoot complex deployment and production issues.
• Mentor other engineers.

Skills

CI/CD workflows

Python programming

Machine learning lifecycle

Cloud ML solutions

Problem solving

Education

Bachelor’s degree in CS/DS/AI or related field

Tools

MLflow

Kubeflow

Azure Machine Learning

Databricks

Terraform

Docker

Kubernetes

Job description

Paradigm, a software company transforming the residential, construction and building product industries, seeks a Senior ML Ops Engineer to join the team in Denver. You will design, build, and maintain scalable MLOps solutions spanning the end-to-end ML lifecycle, including training, deployment, monitoring, and retraining, with a focus on Azure-based workloads.

You will implement model registries, experiment tracking, governance, and CI/CD pipelines, containerize workloads with Docker/Kubernetes,