machine learning engineer for production ML platforms

Lumenalta partners with forward-thinking organizations to build scalable technology solutions that improve user experiences and accelerate business growth. Its global teams focus on curiosity, commitment, transparency, autonomy, and technical excellence.

• Design and maintain MLflow-based workflows for experiment tracking, model registry, versioning, and lifecycle management;
• Build and manage Feature Store infrastructure for reusable and consistent feature pipelines;
• Develop model deployment pipelines with serving infrastructure, A/B testing support, 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, reproducibility, and cost;
• Monitor deployed models for data drift, performance degradation, and system health;
• Trigger automated retraining workflows when needed;
• Collaborate with Data Scientists and Data Engineers to reduce friction between experimentation and production.

• 3–5+ Years of experience in MLOps, ML platform engineering, or DevOps for ML with proven production ML deployments;
• Hands-on expertise with MLflow for tracking, registry, and project management in Databricks or standalone environments;
• Experience building and consuming Feature Store solutions, including Databricks Feature Store or equivalent;
• Experience deploying and serving ML models at scale using real-time and batch inference patterns;
• Ability to design automated pipelines for model training, validation, and deployment with modern CI/CD tooling;
• Strong familiarity with Databricks for distributed training, job orchestration, and cluster management;
• Knowledge of model monitoring practices, including drift detection, alerting, and retraining triggers.

• 100% Dedicated to one project at a time;
• Work with senior-level developers;
• Access to leading technology;
• Must maintain at least a 6-hour overlap with project core business hours, primarily aligned with Central or Eastern U.S. time zones;
• Applications are accepted until August 23, 2026, with feedback expected by August 31, 2026.