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AI/Machine Learning Engineer

Equinix, Inc · Dallas, TX

Publicada em 24/07/2026

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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 SummaryIndependently leads and holds responsibility for multifaceted programs made up of multiple projects and processes; develops and reviews business cases for change and improvement.ResponsibilitiesEngineering Expectations (Core Capabilities)Design and build end-to-end AI systems integrating ML models, LLMs, APIs, and enterprise dataTranslate business requirements into scalable system architectures and solution designsDevelop backend services and API-driven architectures to operationalize intelligent systemsImplement data and model pipelines integrating structured and unstructured data sourcesWork closely with Data Scientists and ML Engineers to integrate model outputs into production systems and decision workflowsApply agent-based workflows and multi-step orchestration to enable complex system behaviorEnsure systems are production-ready-scalable, reliable, secure, and performantContinuously improve system performance through testing, monitoring, and iterative refinementOperating Model & Ways of WorkingWork closely with business, product, Data Science, and engineering teams to co-create solutions and align on outcomesTranslate ambiguous problems into structured requirements and actionable system designsEngage stakeholders regularly to refine solutions and drive adoptionCollaborate across teams to ensure integration with existing systems and enterprise standardsMaintain high-quality documentation and enable knowledge transfer and con
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