Machine Learning Engineer

Lumenalta; Colombia; Presencial; COP 90.000.000 - 150.000.000; Jornada completa

Descripción de la vacante

Lumenalta seeks an experienced MLOps Engineer to operationalize 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.

You will design and maintain MLflow-based workflows, manage Feature Store infrastructure, and develop deployment pipelines with automated testing and rollback strategies.

Formación

• 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.
• 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.

Responsabilidades

• 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.

Conocimientos

MLflow

CI/CD tooling

Model deployment

Monitoring & observability

Collaboration with data teams

Herramientas

Databricks

Databricks Feature Store

MLflow workflows

Descripción del empleo

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.

What You'll Be Doing

• 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.

What We're Looking For

• 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.