Principal Machine Learning Engineer (MLE)

Equinix•Toronto, ON

2d•$200,000 - $300,000

About The Position

As a Principal Machine Learning Engineer, you will design, build, deploy, and scale machine learning and generative AI systems that power real-world products. You will work closely in AI Sidekick team and business teams to translate advanced ML and LLM capabilities into reliable, production‑grade solutions across multi‑cloud environments including GCP, AWS, and Azure. This role blends applied machine learning, software engineering, and MLOps, with a strong focus on building robust, scalable systems rather than purely academic research.

Requirements

PhD with 5+ years, Master’s with 6+ years, or Bachelor’s with 7+ years of experience in Machine Learning, Computer Science, Data Science, or a related field
Strong proficiency in Python for machine learning and production systems
Solid understanding of software engineering fundamentals, system design, and design patterns
Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)
Experience building and deploying production-grade ML systems
Strong communication skills with the ability to explain technical concepts and results to both technical and non-technical stakeholders
Excellent time management, collaboration, and organizational skills

Responsibilities

Design, develop, and deploy machine learning and Large Language Model (LLM)–based solutions for production use cases
Collaborate with Generative AI Center of Excellence leaders and business stakeholders to evaluate buy vs. build decisions for generative AI applications
Develop end-to-end ML pipelines, covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
Architect and implement LLM-powered systems that integrate agents and services across multiple cloud platforms into a unified solution
Optimize ML workflows for performance, scalability, reliability, and cost efficiency in cloud environments (GCP, Azure, AWS)
Implement and maintain MLOps best practices, including CI/CD, model versioning, experiment tracking, and automated retraining
Work extensively with deep learning frameworks such as PyTorch and TensorFlow
Containerize ML services and deploy them using Docker, Kubernetes, App Engine, or virtual machines
Apply strong knowledge of NLP fundamentals, including transformers, attention mechanisms, embeddings, and text preprocessing
Deploy and manage models in production, conduct A/B testing, and measure performance improvements using statistical methods
Develop features, run experiments, analyze results, and translate insights into actionable improvements
Build and deploy classical ML models (regression, classification, clustering), NLP applications (sentiment analysis, summarization, Q&A, chatbots, information retrieval), and computer vision solutions (image classification, object detection, segmentation using models such as YOLOv7, DDRNet, RFTM with datasets like COCO and Cityscapes)