machine learning engineer

Lumenalta partners with forward-thinking organizations to build scalable technology solutions and accelerate business growth.

• Design and maintain MLflow-based workflows for experiment tracking, model registry, versioning, and lifecycle management;
• Build and manage Feature Store infrastructure to enable reusable, consistent feature pipelines;
• Develop model deployment pipelines, including 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 for compute efficiency, reproducibility, and cost;
• Monitor deployed models for data drift, performance degradation, and system health, triggering automated retraining workflows as needed;
• Collaborate with Data Scientists and Data Engineers to reduce friction between experimentation environments and production.

• 3–5+ Years 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 within Databricks or standalone environments;
• Experience building and consuming Feature Store solutions;
• Proven experience deploying and serving ML models at scale, including real-time and batch inference patterns;
• Ability to design automated pipelines for model training, validation, and deployment using 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% Dedication to one project at a time;
• Opportunity to work with a team of senior-level developers;
• Projects utilizing leading technology.