Principal Data Scientist

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 tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective.

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 SummaryAs a Principal ML/AI Engineer, you will lead the design, architecture, and deployment of cutting-edge AI systems from concept to production. You are a full-stack ML practitioner who seamlessly blends advanced software engineering with deep expertise in applied machine learning. Moving beyond pure research, you will build robust, scalable, and production-grade solutions that leverage both classical ML algorithms and state-of-the-art Large Language Models (LLMs) and Agentic frameworks. Acting as a technical anchor, you will drive AI innovation across cloud environments and set the standard for ML engineering excellence.

Our Engineering Environment & CultureYou will join a fast-paced, agile environment where rapid iteration meets engineering rigor. We operate on a modern, cloud-native infrastructure, heavily utilizing containerized microservices and robust CI/CD pipelines. This ensures our machine learning models-whether classical algorithms or complex agentic LLM workflows-are deployed quickly, safely, and at enterprise scale. Beyond building highly reliable production systems, we deeply value intellectual curiosity and foundational innovation. We don't just consume AI; we actively contribute to the field. Our teams publish research papers on novel methodologies, and we foster a culture that encourages exploring the unknown. In this role, you will find the rare balance of driving high-impact production engineering while having support to research new AI approaches and author future publications.

ResponsibilitiesTechnical Leadership & Architecture:

Lead the end-to-end design and architecture of scalable, production-ready AI/ML systems, integrating them seamlessly into broader enterprise architectures

Full-Stack AI Development:

Architect, build, and deploy a diverse portfolio of AI solutions-ranging from classical ML models (regression, classification, clustering) to advanced generative AI and Agentic applications

Production Engineering:

Write exceptionally clean, optimized, and maintainable production code. Apply strong software engineering fundamentals to bridge the gap between data science and robust software engineering

Cloud & MLOps Infrastructure:

Design and optimize ML workflows and pipelines in cloud environments (GCP, AWS, or Azure). Implement rigorous MLOps practices, including CI/CD, continuous training, model tracking, and automated deployment

R D and Innovation:

Evaluate and integrate the newest frameworks and methodologies in the LLM/Agentic space. Influence senior leadership on \