Principal ML Engineer: Production AI/LLM Systems

Equinix; Toronto; On-site; CAD 154,000 - 232,000; Full time

Benefits offered by this job

Employee Assistance Program

Canada Core Benefits

Job summary

Equinix is seeking a Principal Machine Learning Engineer to design, build, deploy, and scale ML/LLM systems powering real-world products across GCP, AWS, and Azure. Join the AI Sidekick team to translate advanced ML into robust, production-grade solutions with a strong emphasis on scalable systems and MLOps.

You will work with Generative AI CoE leaders and business stakeholders to evaluate buy vs. build decisions and develop end-to-end ML pipelines including data ingestion, training, evaluation,

Qualifications

• PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years in ML/CS.
• Strong Python for ML and production systems.
• Experience deploying production-grade ML systems and cloud platforms.

Responsibilities

• Design, develop, and deploy ML/LLM-based solutions for production use.
• Collaborate with Generative AI leaders to evaluate buy vs. build decisions.
• Develop end-to-end ML pipelines from data to deployment and monitoring.
• Architect LLM-powered systems across multi-cloud environments.
• Optimize workflows for performance, reliability, and cost.
• Implement MLOps practices including CI/CD and model versioning.

Skills

Python

Cloud platforms

PyTorch

TensorFlow

MLOps

CI/CD

Docker

Kubernetes

NLP transformers

Education

PhD

Master’s

Bachelor’s

Tools

Docker

Kubernetes

App Engine

Job description

Equinix is seeking a Principal Machine Learning Engineer to design, build, deploy, and scale ML/LLM systems powering real-world products across GCP, AWS, and Azure. Join the AI Sidekick team to translate advanced ML into robust, production-grade solutions with a strong emphasis on scalable systems and MLOps.

You will work with Generative AI CoE leaders and business stakeholders to evaluate buy vs. build decisions and develop end-to-end ML pipelines including data ingestion, training, evaluation,