Senior MLOps Engineer

Judge Group, Inc.; Phoenix (AZ); On-site; USD 90,000 - 96,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Job summary

Judge Group, Inc. is seeking a Senior MLOps Engineer to build and scale an enterprise ML operations platform that enables reliable, secure, and production-ready AI/ML solutions.

You will establish frameworks, automation, governance, and deployment standards across the organization. Collaboration with data scientists, data engineers, and platform teams will transform experimental models into scalable services, ensuring reproducibility and observability while driving CI/CD practices and

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 5+ years in Machine Learning Engineering, MLOps, Platform Engineering, Software Engineering, or related discipline.
• Experience building, deploying, and supporting production machine learning systems.
• Experience developing solutions using Python and SQL.
• Experience implementing CI/CD pipelines, automated testing, and deployment practices in machine learning environments.
• Experience with model lifecycle management, including deployment, monitoring, versioning, governance, retraining, and drift detection.
• Experience designing and maintaining scalable data and feature pipelines.
• Experience working with cloud platforms and cloud-native services.
• Ability to communicate technical concepts effectively and collaborate with cross-functional engineering and business stakeholders.

Responsibilities

• Design, develop, and scale an enterprise MLOps platform that supports the full machine learning lifecycle.
• Build reusable frameworks and automation for model training, validation, deployment, monitoring, retraining, and maintenance.
• Create standardized workflows that enable machine learning models to consume trusted and governed data products.
• Establish model lifecycle management practices, including versioning, approvals, deployment promotion, rollback strategies, and lineage tracking.
• Partner with Data Science teams to accelerate the path from experimentation to production.
• Implement monitoring and observability capabilities for model performance, drift detection, data quality, bias evaluation, and platform reliability.
• Develop automated retraining and refresh processes using orchestration and workflow management technologies.
• Collaborate with Data Engineering teams to build reliable feature engineering and data preparation pipelines that align with enterprise architecture standards.
• Define and implement CI/CD practices for machine learning assets, including code, models, configurations, and data dependencies.
• Establish governance standards that support security, compliance, auditability, reproducibility, and responsible AI initiatives.
• Create technical documentation, operational runbooks, implementation standards, and developer enablement resources.
• Contribute to the evolution of the platform from an initial framework into a mature, enterprise-scale capability.

Skills

Python

SQL

CI/CD pipelines

Cloud platforms

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related field

Job description

Location: Phoenix, AZ Salary: $65.00 USD Hourly – $70.00 USD Hourly

Senior MLOps Engineer

About the Role

We are seeking a Senior MLOps Engineer to help build and scale an enterprise Machine Learning Operations platform that enables the delivery of reliable, secure, and production-ready AI/ML solutions. In this role, you will establish the foundational frameworks, automation, governance, and deployment standards that support machine learning across the organization.

You will partner closely with Data Scientists, Data Engineers, and platform teams to transform experimental models into scalable services, ensuring machine learning solutions are reproducible, observable, and operationalized within a modern cloud-based data ecosystem. This is an opportunity to influence architecture, define best practices, and help shape the future of enterprise AI adoption.

Responsibilities

• Design, develop, and scale an enterprise MLOps platform that supports the full machine learning lifecycle.
• Build reusable frameworks and automation for model training, validation, deployment, monitoring, retraining, and maintenance.
• Create standardized workflows that enable machine learning models to consume trusted and governed data products.
• Establish model lifecycle management practices, including versioning, approvals, deployment promotion, rollback strategies, and lineage tracking.
• Partner with Data Science teams to accelerate the path from experimentation to production.
• Implement monitoring and observability capabilities for model performance, drift detection, data quality, bias evaluation, and platform reliability.
• Develop automated retraining and refresh processes using orchestration and workflow management technologies.
• Collaborate with Data Engineering teams to build reliable feature engineering and data preparation pipelines that align with enterprise architecture standards.
• Define and implement CI/CD practices for machine learning assets, including code, models, configurations, and data dependencies.
• Establish governance standards that support security, compliance, auditability, reproducibility, and responsible AI initiatives.
• Create technical documentation, operational runbooks, implementation standards, and developer enablement resources.
• Contribute to the evolution of the platform from an initial framework into a mature, enterprise-scale capability.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
• 5 years of experience in Machine Learning Engineering, MLOps, Platform Engineering, Software Engineering, or a related discipline.
• Experience building, deploying, and supporting production machine learning systems.
• Experience developing solutions using Python and SQL.
• Experience implementing CI/CD pipelines, automated testing, and deployment practices in machine learning environments.
• Experience with model lifecycle management, including deployment, monitoring, versioning, governance, retraining, and drift detection.
• Experience designing and maintaining scalable data and feature pipelines.
• Experience working with cloud platforms and cloud-native services.
• Ability to communicate technical concepts effectively and collaborate with cross-functional engineering and business stakeholders.

Medical, dental, and vision insurance are available to qualified candidates who meet eligibility requirements.