AI/ML Ops Engineer (Mid) - Production Pipelines & Cloud

Motion Recruitment; Chicago (IL); On-site; USD 110,000 - 170,000; Full time

Benefits offered by this job

Medical, Dental, Vision Insurance

Vacation Time

Stock Options

Job summary

Motion Recruitment is seeking a Mid-Level AI/ML Ops Engineer to design and maintain CI/CD pipelines for deploying ML models at scale in cloud environments. You will work on Databricks and AWS Bedrock, leverage Docker and Kubernetes, and ensure robust monitoring and observability across production systems.

The role requires hands-on ML deployment experience, strong Python skills, and a solid grounding in data analytics.

Qualifications

• Bachelor’s degree or comparable experience in CS/Engineering.
• 3–5 years in DevOps, MLOps, data infra, or related roles.
• Hands-on experience deploying ML models to production.
• Experience with Databricks and AWS Bedrock.
• Docker and Kubernetes or equivalent orchestration.
• Python proficiency and CI/CD tooling.
• Experience with monitoring/observability for production systems.
• Understanding of data analytics fundamentals.

Responsibilities

• Build and maintain CI/CD pipelines for deploying AI/ML models into production.
• Implement monitoring to catch performance degradation, drift, and failures.
• Manage cloud infrastructure supporting AI workloads, including containerized services and cloud AI platforms.
• Collaborate with AI Engineers and the AI Tech Lead to translate requirements into deployment plans.
• Troubleshoot production issues and support model versioning, reproducibility, and rollback.
• Monitor and optimize the cost and resource efficiency of AI/ML workloads.

Skills

DevOps/MLOps

Python

CI/CD tooling

Monitoring & observability

Data analytics fundamentals

Education

Bachelor’s degree in Computer Science/Engineering or related field

Tools

Databricks

Bedrock (AWS)

Docker

Kubernetes

AWS

Job description

Motion Recruitment is seeking a Mid-Level AI/ML Ops Engineer to design and maintain CI/CD pipelines for deploying ML models at scale in cloud environments. You will work on Databricks and AWS Bedrock, leverage Docker and Kubernetes, and ensure robust monitoring and observability across production systems.

The role requires hands-on ML deployment experience, strong Python skills, and a solid grounding in data analytics.