Machine Learning Engineer

Lumenalta; United States; Remote; PHP 4,297,114 - 6,138,735; Full time

Benefits offered by this job

Dedicated project focus

Senior-level team

Leading tech projects

Job summary

Lumenalta is seeking an experienced MLOps Engineer to operationalize machine learning on the Databricks platform. This is a future opportunity role where you will bridge data engineering and ML, building an infrastructure for model deployments.

Ideal candidates should have 3 to 5+ years of experience in MLOps, hands-on knowledge of MLflow, and the ability to design automated pipelines. This remote position is flexible with at least a 6-hour overlap with U.S. business hours.

Qualifications

• 3-5+ years in MLOps or ML platform engineering.
• Hands-on expertise with MLflow in Databricks.
• Experience deploying ML models at scale.

Responsibilities

• Design and maintain MLflow-based workflows.
• Build and manage Feature Store infrastructure.
• Develop model deployment pipelines.

Skills

MLOps

MLflow

Feature Store

CI/CD

Databricks

Job description

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.

Future Opportunity Role – Talent Pipeline

This is a future opportunity role. We continuously meet talented engineers to support upcoming client projects. While there may not be an immediate opening, qualified candidates may be considered for future engagements.

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.
• Strong familiarity with Databricks for distributed training, job orchestration, and cluster management.
• Knowledge of model monitoring practices, including drift detection, alerting, and retraining triggers.

Why Lumenalta is an amazing place to work at

At Lumenalta, you can expect that you will:

• 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.

This is a fully remote position open to candidates based in Latin America (LATAM). While location is flexible, candidates must be willing to maintain at least a 6‑hour overlap with core business hours, which are primarily aligned with the Pacific, Central, or Eastern U.S. time zones to ensure effective collaboration with project teams.

Application Deadline

This role is a future opportunity position with no predetermined start date. Applications will be accepted until June 14, 2026. As we continue to build our talent pipeline, the position may be reposted to allow us to connect with additional qualified professionals.