Director of AI Engineering, Equinix

• 12+ years in software engineering, data engineering, or ML engineering
• 5+ years leading large, distributed engineering teams (including managers of managers)
• Proven track record of delivering ML/AI systems at scale in production environments
• Deep knowledge of machine learning systems, MLOps, and cloud-native architectures
• Experience with ML frameworks (e.g., TensorFlow, PyTorch) and data platforms
• Strong understanding of GenAI/LLMs, prompt engineering, and retrieval-augmented systems
• Familiarity with distributed systems, APIs, and microservices architecture
• Strong ability to translate business strategy into technical execution
• Experience driving large-scale transformation initiatives
• Excellent communication and stakeholder management skills

Desirable

• We are seeking a Director of AI Engineering to lead and scale a high-performing Machine Learning Engineering (MLE) organization
• This leader will be responsible for building production-grade AI/ML systems that power next-generation generative and predictive capabilities across the enterprise
• The role combines deep technical leadership, organizational scale, and strong business alignment to translate AI innovation into measurable impact
• Reporting to Yang Song within Digital and Innovation Office, this role will work closely with the Data & Engineering team around technology and development
• This is a hands-on technical leadership role responsible for building and scaling production-grade AI systems
• This role is critical to transforming AI from experimentation into a scalable, enterprise capability
• You will define how AI is built, deployed, and leveraged across the organization—unlocking faster decisions, smarter automation, and sustained competitive advantage
• Lead and Scale the MLE Organization
• Build, lead, and mentor a global team of Machine Learning Engineers and technical leaders
• Establish a high-performance engineering culture focused on quality, velocity, and accountability
• Drive hiring, onboarding, and career development for MLE talent across regions
• Deliver Production-Grade AI/ML Systems
• Own end-to-end delivery of ML platforms, pipelines, and services (training, inference, monitoring)
• Operationalize models into scalable, reliable, and secure production systems
• Partner with Data Science and Product to move from experimentation to deployment
• Define AI Engineering Strategy & Architecture
• Set the vision for ML platform architecture, MLOps, and GenAI enablement
• Standardize tools, frameworks, and best practices for model development and deployment
• Ensure systems are built for scale, performance, and cost efficiency
• Drive Generative AI and Advanced Analytics
• Lead development of GenAI capabilities (LLMs, RAG, copilots, automation workflows)
• Enable reusable AI services and APIs to accelerate use case delivery
• Stay ahead of industry trends and translate them into enterprise-ready capabilities
• Cross-Functional Leadership & Stakeholder Alignment
• Partner with Product, Data, Engineering, and Business leaders to prioritize high-impact use cases
• Communicate strategy, progress, and outcomes to executive stakeholders
• Align AI initiatives with business goals, including revenue growth, efficiency, and customer experience
• Governance, Risk, and Responsible AI
• Establish best practices for model governance, monitoring, and lifecycle management
• Ensure compliance with security, privacy, and ethical AI standards
• Implement guardrails for safe and responsible use of AI technologies