MLOps Engineer

VALCE Talent Solutions

Machine Learning Engineer joining MarTech team to develop and deploy ML models and pipelines. Focus on driving innovation within ML ecosystem for improved customer experience.

Posted 5/21/2026full-timeRemote • 🇲🇽 MexicoMid-LevelSeniorWebsite

About the role

Key responsibilities & impact

Design, develop, and deploy machine learning solutions and feature engineering pipelines.
Configure, test, debug, deploy, document, and maintain ML pipelines, models and feature engineering modules while adhering to specific development best practices and quality standards.
Work closely with data scientists, data engineers, and solution architects to develop technical design specifications for ML programs, focusing on efficient feature engineering and model deployment.
Analyze large-scale datasets and validate the proposed ML solutions with both the architectural design and the business needs, ensuring model performance meets target metrics.
Responsible for troubleshooting and issue analysis across the ML stack, including feature pipelines, model training, inference, and model monitoring, as well as coding, testing, and implementing model enhancements.
Demonstrate a strong understanding of supervised, unsupervised, ensemble, and deep learning algorithms to design and implement effective ML solutions, with experience in feature engineering, model evaluation, and continuous performance optimization to meet business targets.
Implement and maintain MLOps practices including experiment tracking, model versioning, A/B testing, and automated retraining pipelines.
Thrive in a fast-paced agile development environment, driving iterative model improvements.
Implement and maintain data governance and model monitoring frameworks to ensure model reliability, fairness, and compliance with business standards.
Available to support/unblock planned model deployments and retraining cycles during off hours.
Contribute to the evolution of our ML architecture, with a focus on MLOps principles and emerging technologies for feature stores.

Requirements

What you’ll need

4+ years of professional experience in a ML engineering capacity with focus on production ML systems.
Bachelor's or master's degree in Machine Learning, information technology, Computer Science, or equivalent experience.
Good communication skill (verbal and written)
Experienced on Agile methodology and tools (Jira, Confluence)
Work experience in the Retail industry is a plus

Benefits

Comp & perks

competitive salary
flexible work arrangements
professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningfeature engineeringmodel deploymentsupervised learningunsupervised learningensemble learningdeep learningMLOpsmodel evaluationdata governance

Soft Skills

communicationtroubleshootingproblem analysiscollaborationadaptabilityagile developmentiterative improvementdocumentationanalytical thinkingattention to detail

Certifications

Bachelor's degree in Machine LearningMaster's degree in Machine LearningBachelor's degree in Computer ScienceMaster's degree in Computer Scienceequivalent experience