Machine Learning Engineer

About the role

Lumenalta is looking for an experienced MLOps Engineer to operationalize machine learning at scale on the Databricks platform. The role bridges data engineering and ML, building the infrastructure and workflows that move models from experimentation to reliable production.

Key responsibilities

• Design and maintain MLflow‑based workflows for experiment tracking, model registry, versioning, and lifecycle management.
• Build and manage Feature Store infrastructure to provide reusable, consistent feature pipelines.
• Develop model deployment pipelines with serving infrastructure, A/B testing support, versioning, and rollback strategies.
• Implement CI/CD pipelines tailored for ML workflows, including automated testing, validation gates, and deployment triggers.
• Orchestrate distributed model training on Databricks, optimizing compute efficiency, reproducibility, and cost.
• Monitor deployed models for data drift, performance degradation, and system health, triggering automated retraining as needed.
• Collaborate with Data Scientists and Data Engineers to reduce friction between experimentation and production environments.

Required profile

• 3–5+ years of experience in MLOps, ML platform engineering, or DevOps for ML.
• Proven track record of production ML deployments at scale.
• Hands‑on expertise with MLflow in Databricks or similar environments.
• Experience building and consuming Feature Store solutions.
• Strong familiarity with Databricks for distributed training and cluster management.

Required skills

• MLflow
• Databricks
• Feature Store
• CI/CD pipelines
• A/B testing
• Distributed training
• Model monitoring and data drift detection

What we offer

• Fully remote work with flexible location in Latin America.
• Dedicated focus on one project at a time to foster innovation.
• Collaboration with senior‑level developers and cutting‑edge technologies.
• Alignment with core business hours for effective teamwork across US time zones.