Remote MLOps Engineer: Scale Production ML Pipelines

OPEN OPENLANE US Inc; Carmel (IN); Remote; USD 110,000 - 165,000; Full time

Benefits offered by this job

Medical, dental, and vision benefits

401K with company match

Paid vacation and sick time

Parental leave

Employee Assistance Program

Tuition reimbursement

Volunteer day

Job summary

OPENLANE is hiring a remote MLOps Engineer to build and operate automated ML pipelines, from data ingestion to deployment and rollback. You will ensure reproducibility and scalable, cost-efficient production of models, with strong emphasis on observability and incident response.

You will collaborate with Data Scientists, Data Engineers, Software Engineers, and Infrastructure teams, while leveraging AI-assisted tooling to improve pipelines and platform tooling.

Qualifications

• 3+ years of experience in MLOps, ML engineering, SRE, DevOps, or software engineering.
• Bachelor’s degree preferred or equivalent practical experience.
• Strong Python skills and hands-on experience with a major cloud platform (AWS preferred).
• Experience with workflow orchestration, infrastructure-as-code, and CI/CD systems.
• Experience with containers and Kubernetes and monitoring tools.
• Hands-on experience with system debugging, observability, and incident response in production environments.

Responsibilities

• Design, build, and operate automated ML pipelines from ingestion to deployment and rollback.
• Implement Model CI/CD, canarys and safe release strategies.
• Monitor model performance, data drift, and latency with actionable alerts.
• Drive reproducibility via versioned code, data, features, and environments.
• Collaborate with Data Scientists, Engineers, and Infrastructure teams.

Skills

MLOps

Python

Cloud platforms

CI/CD

Site Reliability Engineering

DevOps

Software Engineering

Education

Bachelor's degree preferred or equivalent practical experience

Tools

Airflow

Kubeflow

Terraform

Docker

Kubernetes

Job description

OPENLANE is hiring a remote MLOps Engineer to build and operate automated ML pipelines, from data ingestion to deployment and rollback. You will ensure reproducibility and scalable, cost-efficient production of models, with strong emphasis on observability and incident response.

You will collaborate with Data Scientists, Data Engineers, Software Engineers, and Infrastructure teams, while leveraging AI-assisted tooling to improve pipelines and platform tooling.