Machine Learning Engineer (MLOps)

About the role

We are 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 across teams.
• Develop model deployment pipelines, including serving infrastructure, A/B testing support, versioning, and rollback strategies.
• Implement CI/CD pipelines tailored for ML workflows with 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 with proven production deployments.
• Hands‑on expertise with MLflow in Databricks or standalone environments.
• Experience building and consuming Feature Store solutions (Databricks Feature Store or equivalent).
• Demonstrated ability to deploy and serve ML models at scale, both real‑time and batch.
• Proficiency designing automated pipelines for model training, validation, and deployment using modern CI/CD tools.
• Strong familiarity with Databricks for distributed training, job orchestration, and cluster management.
• Knowledge of model monitoring practices, including drift detection, alerting, and retraining triggers.

Required skills

• MLflow
• Databricks
• Feature Store
• CI/CD tooling
• Model monitoring
• A/B testing
• Distributed training orchestration

What we offer

• Fully remote work for candidates based in Latin America with a minimum 6‑hour overlap with US business hours.
• Opportunity to focus 100% on one project at a time, fostering innovation and growth.
• Collaboration with a team of senior‑level developers using leading technologies.