MLOps Engineer — Databricks & MLflow, Remote

Lumenalta; United States; Remote; CAD 110,000 - 192,000; Full time

Benefits offered by this job

Flexible working hours in a remote env

Health insurance (medical and dental)

Professional development fund

15 days paid time off annually

Soft-skill development courses

Job summary

Lumenalta is hiring an experienced MLOps Engineer to operationalize ML at scale on the Databricks platform. You will bridge data engineering and ML, building infrastructure and workflows to move models from experimentation to production deployments.

You will design MLflow-based workflows, build Feature Store pipelines, and implement CI/CD for ML. Expect collaboration with data scientists and engineers in a fully remote role across North America.

Qualifications

• 3-5+ years in MLOps, ML platform engineering, or DevOps for ML.
• Hands-on with MLflow for tracking, registry, and projects.
• Experience building/consuming Feature Store solutions.
• Proven experience deploying and serving ML models at scale.
• Automated pipelines for training, validation and deployment using CI/CD.
• Strong Databricks experience for distributed training and orchestration.
• Knowledge of drift monitoring, alerting, and retraining triggers.

Responsibilities

• Design and maintain MLflow-based workflows for experiment tracking and registry.
• Build Feature Store infrastructure for reusable feature pipelines.
• Develop model deployment pipelines with serving infra and rollback strategies.
• Implement ML-focused CI/CD pipelines with automated testing and gates.
• Orchestrate distributed model training on Databricks for efficiency and reproducibility.
• Monitor deployed models for drift and performance; trigger retraining when needed.
• Collaborate with data scientists and engineers to reduce friction between environments.

Tools

MLflow

Databricks

Feature Store

CI/CD for ML

Model deployment

Monitoring

Job description

Lumenalta is hiring an experienced MLOps Engineer to operationalize ML at scale on the Databricks platform. You will bridge data engineering and ML, building infrastructure and workflows to move models from experimentation to production deployments.

You will design MLflow-based workflows, build Feature Store pipelines, and implement CI/CD for ML. Expect collaboration with data scientists and engineers in a fully remote role across North America.