Machine Learning Engineer

Lumenalta partners with organizations to build scalable technology solutions and is seeking an experienced MLOps Engineer for future client engagements. The role focuses on operationalizing machine learning at scale on Databricks by building MLflow workflows, feature infrastructure, deployment pipelines, CI/CD automation, monitoring, and retraining processes.

Responsibilities:

• 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 across teams and use cases
• 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
• Be 100% dedicated to one project at a time so that you can innovate and grow
• Be a part of a team of talented and friendly senior-level developers
• Work on projects that allow you to use leading tech

Requirements:

• 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 (Databricks Feature Store or equivalent)
• 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
• This is a fully remote position; however, candidates must be based in regions that align with the Pacific, Central, or Eastern U.S. time zones to ensure effective collaboration with client and team schedules