End-to-End ML Engineer — Hybrid (Monterrey)

Corning Inc.; Monterrey; Híbrido; MXN 240.000 - 420.000; Jornada completa

Descripción de la vacante

Corning Inc. in Monterrey is seeking a Machine Learning Engineer to join a collaborative technical team. You will build, deploy, monitor, and maintain ML solutions that create measurable business value, partnering with data scientists, IT, analytics, and manufacturing.

Based in Monterrey with regular onsite presence and hybrid flexibility, you will contribute to end-to-end pipelines, model serving, and MLOps practices while documenting work and sharing learnings with the team.

Formación

• 0-2 years of experience in ML, data science, data engineering, software engineering, or related projects.
• Strong Python foundation and experience with ML libraries.
• Ability to explain project contributions, tools used, and outcomes.
• Advanced technical and business English, both written and verbal.
• Willingness to work onsite in Monterrey at least two days per week.

Responsabilidades

• Develop end-to-end ML pipelines, including ingestion, training, validation, deployment, monitoring, and retraining.
• Collaborate with data scientists and engineers to productionize prototypes.
• Support model serving via APIs, batch jobs, or real-time systems with MLOps practices.
• Troubleshoot data, model, deployment, and integration issues and maintain documentation.

Conocimientos

Python

ML libraries

Data pipelines

English communication

Educación

Bachelor's degree

Herramientas

Docker

Kubernetes

MLflow

Kubeflow

Descripción del empleo

Corning Inc. in Monterrey is seeking a Machine Learning Engineer to join a collaborative technical team. You will build, deploy, monitor, and maintain ML solutions that create measurable business value, partnering with data scientists, IT, analytics, and manufacturing.

Based in Monterrey with regular onsite presence and hybrid flexibility, you will contribute to end-to-end pipelines, model serving, and MLOps practices while documenting work and sharing learnings with the team.