Senior ML Engineer - Production Systems & MLOps Champion

Corning Incorporated; Monterrey; Híbrido; MXN 600.000 - 900.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive benefits

High-impact projects

Global collaboration

Career development

Accommodations on request

Descripción de la vacante

Corning Incorporated in Monterrey is seeking a Senior Machine Learning Engineer to design, deploy, and maintain robust ML systems for manufacturing and business functions. This individual contributor role combines data science with production readiness, focusing on scalable APIs, batch and real‑time processing, and secure deployment.

You will drive MLOps practices, versioning, CI/CD, containerization, and monitoring, while guiding junior contributors and collaborating with data scientists, IT,

Formación

• Degree in a technical field and 3+ years in ML/DS or software engineering.
• Hands-on experience deploying ML models in production.
• Strong Python and ML framework knowledge (scikit-learn, TensorFlow, PyTorch).

Responsabilidades

• Design, build, and maintain end-to-end ML pipelines (ingestion, training, deployment, monitoring).
• Lead production translation of prototypes and contribute to APIs, batch and real-time systems.
• Implement MLOps and DevOps practices for versioning, CI/CD, containerization, and security.
• Guide junior contributors and collaborate with data scientists, IT, analytics, and manufacturing.

Conocimientos

Python

ML concepts

CI/CD

MLOps

Data pipelines

English

Educación

Bachelor's/Master's in CS/Engineering/DS

Herramientas

Databricks

MLflow

Kubeflow

Docker

Kubernetes

APIs

Cloud platforms

Descripción del empleo

Corning Incorporated in Monterrey is seeking a Senior Machine Learning Engineer to design, deploy, and maintain robust ML systems for manufacturing and business functions. This individual contributor role combines data science with production readiness, focusing on scalable APIs, batch and real‑time processing, and secure deployment.

You will drive MLOps practices, versioning, CI/CD, containerization, and monitoring, while guiding junior contributors and collaborating with data scientists, IT,