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Principal Machine Learning Engineer

Equinix
2 days ago
Full-time
On-site
Toronto, Canada
AI and Machine Learning
  • As a 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
  • 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)