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# Machine Learning Engineer

- **Company:** Clevertech Partners, LLC
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Automation of Tests, Continuous Integration, Information Engineering, DevOps, Distributed Computing Environment, Machine Learning, Azure Machine Learning, Management of Software Versions, Machine Learning Operations, Databricks
- **Published:** August 24, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pecgbyev5c

## About the Role

* 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 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.

## Description

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.


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