Machine Learning Engineer

About the role

We are looking for an experienced MLOps Engineer to operationalize machine‑learning models at scale on the Databricks platform. The role bridges data engineering and ML, building the infrastructure that moves 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, including serving infrastructure, A/B testing, versioning and rollback strategies.
• Implement CI/CD pipelines for ML workflows with automated testing, validation gates and deployment triggers.
• Orchestrate distributed model training on Databricks, optimizing compute efficiency 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.

Required profile

• 3–5+ years of experience in MLOps, ML platform engineering or DevOps for ML.
• Proven track record of production ML deployments.
• Hands‑on expertise with MLflow in Databricks or standalone environments.
• Experience building and consuming Feature Store solutions.
• Strong familiarity with Databricks for distributed training, job orchestration and cluster management.
• Knowledge of model monitoring practices, including drift detection and automated retraining.

Required skills

• MLflow
• Databricks
• Feature Store (Databricks or equivalent)
• CI/CD tooling for ML pipelines
• Model monitoring and drift detection
• Distributed training orchestration
• A/B testing and model versioning
• Rollback and automated retraining strategies

What we offer

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