Principal Machine Learning Engineer

Who are we?

Equinix is the world's digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.

A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.

Help us challenge assumptions, uncover bias, and remove barriers-because progress starts with fresh ideas. You'll find belonging, purpose, and a team that welcomes you-because when you feel valued, you're empowered to do your best work.

Job Summary

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
• Build and integrate agent-based workflows using platforms such as Google Agentspace, Microsoft Copilot, and Salesforce Agentforce
• 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
• (Good to have) 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)

• 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