MLOps Engineer: Cloud AI Deployments & Platform Engineering

Motion Recruitment; Detroit (MI); Remote; USD 55,000 - 96,000; Full time

Benefits offered by this job

Medical, Dental, and Vision Insurance

Career Development Opportunities

Job summary

Motion Recruitment in Detroit is seeking an AI/ML Operations Engineer to deploy and scale production AI systems using AWS, Databricks, and containerized environments. You will build and maintain CI/CD pipelines, manage Kubernetes, and ensure robust monitoring and reliability across enterprise workloads.

The role emphasizes real-world AI deployment, collaboration with development teams, and opportunities to influence best practices as the AI program grows.

Qualifications

• 3+ years of experience in MLOps, Platform Engineering, or Infrastructure Engineering.
• Experience deploying machine learning models into production.
• Strong Python development and scripting skills.
• Hands-on experience with AWS cloud services.
• Experience with Databricks or comparable data/AI platforms.
• Experience building and maintaining CI/CD pipelines.
• Docker and Kubernetes experience.
• Monitoring, observability, and incident response experience.

Responsibilities

• Deploy and manage ML infrastructure on AWS and Databricks.
• Develop and maintain CI/CD pipelines for ML workloads.
• Manage Kubernetes and container orchestration for production systems.
• Implement monitoring, observability, and reliability practices.
• Collaborate with cross-functional teams to scale AI capabilities.

Skills

MLOps

Python

AWS

Databricks

CI/CD pipelines

Docker

Kubernetes

Monitoring & observability

Incident response

Education

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

Tools

Docker

Kubernetes

Job description

Motion Recruitment in Detroit is seeking an AI/ML Operations Engineer to deploy and scale production AI systems using AWS, Databricks, and containerized environments. You will build and maintain CI/CD pipelines, manage Kubernetes, and ensure robust monitoring and reliability across enterprise workloads.

The role emphasizes real-world AI deployment, collaboration with development teams, and opportunities to influence best practices as the AI program grows.