Senior Machine Learning Engineer - Production ML & MLOps

Corning Inc.; Monterrey; Hybrid; MXN 700,000 - 900,000; Full time

Benefits offered by this job

Competitive benefits

Global collaboration

Career development opportunities

Job summary

Corning Inc. in Monterrey seeks a Senior Machine Learning Engineer to design, deploy, and maintain production-grade ML systems that boost manufacturing performance.

This role is based in Monterrey with regular onsite presence and hybrid flexibility, offering opportunities to influence architecture, MLOps practices, and technical standards while collaborating with data scientists, IT, analytics, and manufacturing teams.

Qualifications

• Bachelor's or Master's degree in a technical field with 3+ years in ML/data/engineering.
• Strong Python and ML framework experience; production deployment知.
• Ability to influence architecture and coach teammates without direct reports.
• Excellent English; onsite in Monterrey at least two days a week.

Responsibilities

• Design, build, and maintain end-to-end ML pipelines including ingestion, training, deployment, monitoring, and retraining.
• Lead translation of prototypes into scalable production solutions and shape architecture for APIs and real-time systems.
• Implement MLOps/DevOps practices for versioning, CI/CD, orchestration, security, and reliability.
• Mentor junior contributors, perform code reviews, and collaborate with data scientists, IT, analytics, and manufacturing teams.

Skills

Python programming

ML lifecycle & MLOps

CI/CD & DevOps

System troubleshooting

Education

Bachelor's or Master's degree in a relevant technical field

Tools

scikit-learn

TensorFlow

PyTorch

Databricks

MLflow

Kubeflow

Docker

Kubernetes

Job description

Corning Inc. in Monterrey seeks a Senior Machine Learning Engineer to design, deploy, and maintain production-grade ML systems that boost manufacturing performance.

This role is based in Monterrey with regular onsite presence and hybrid flexibility, offering opportunities to influence architecture, MLOps practices, and technical standards while collaborating with data scientists, IT, analytics, and manufacturing teams.