Remote MLOps Engineer — Databricks & Production ML

Lumenalta; United States; Remote; PHP 6,293,000 - 9,440,000; Full time

Job summary

Lumenalta is seeking an experienced MLOps Engineer to operationalize machine learning at scale on the Databricks platform. You’ll bridge data engineering and ML, building infrastructure and workflows that move models from experimentation to production deployments.

Ideal candidates have 3-5+ years in MLOps, hands-on MLflow, and experience with feature stores and scalable deployment patterns. This fully remote role supports LATAM with core-time overlap to align with Pacific/Central/Eastern U.S.

Qualifications

• 3-5+ years in MLOps or DevOps for ML with production deployments.
• Hands-on MLflow experience in Databricks or standalone environments.
• Experience building feature stores (Databricks Feature Store or equivalent).
• Experience deploying ML models at scale with real-time and batch inference.

Responsibilities

• Design and maintain MLflow workflows for experiment tracking and registry.
• Build Feature Store infrastructure for reusable feature pipelines.
• Develop model deployment pipelines with serving, A/B testing, versioning, rollback.
• Implement CI/CD pipelines for ML with automated testing and validation gates.
• Orchestrate distributed model training on Databricks for efficiency and reproducibility.
• Monitor models for drift and performance, triggering retraining.
• Collaborate with data scientists and data engineers to reduce friction between prod and dev.

Skills

MLflow

Databricks

Feature Store

CI/CD for ML

Model monitoring

MLOps

Tools

Databricks

CI/CD tooling

Monitoring tooling

Job description

Lumenalta is seeking an experienced MLOps Engineer to operationalize machine learning at scale on the Databricks platform. You’ll bridge data engineering and ML, building infrastructure and workflows that move models from experimentation to production deployments.

Ideal candidates have 3-5+ years in MLOps, hands-on MLflow, and experience with feature stores and scalable deployment patterns. This fully remote role supports LATAM with core-time overlap to align with Pacific/Central/Eastern U.S.