Machine learning engineer

At Lumenalta, we partner with forward-thinking organizations to build technology solutions that scale, delight users, and accelerate business growth. Our global teams bring curiosity, commitment, and technical excellence to every project. We value transparency, autonomy, and impact—empowering every team member to do their best work. We’re seeking an experienced MLOps Engineer responsible for operationalizing machine learning at scale on the Databricks platform. This role bridges data engineering and ML, building the infrastructure and workflows that take models from experimentation to reliable production deployments.

• 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.

• 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.

• 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.