Director, AI Engineering

About the Role

We are seeking an experienced and visionary technology leader to lead the development and scaling of our Operational AI platform capabilities. This role will own the strategy, architecture, delivery, and operational excellence of Couchbase AI Cloud Platform capabilities. This is a strategic leadership role at the intersection of distributed systems, cloud-native platforms, and AI. You will lead a large, multi-layered engineering organization responsible for delivering The Operational Data Platform for AI, while partnering closely with Product, Design, SRE, and Go-To-Market teams. Your leadership will directly influence company growth, customer adoption, platform reliability, and Couchbase’s competitive position in the market. This role requires a balance of strategic vision, organizational leadership, and technical depth.

Key Responsibilities

• Lead and grow high-performing engineering teams focused on building the Operational Data platform for AI.
• Drive architectural decisions for multiple platform capabilities at the intersection of Databases, PaaS and AI.
• Ensure production-grade scalability for large-scale workloads.
• Partner closely with Product, Field, Support, and Customer Success to deliver differentiated enterprise AI capabilities.
• Balance innovation with operational rigour and ensuring reliability, performance, and security standards are met.
• Build strong cross-functional collaboration with core database, storage, and cloud infrastructure teams.
• Partner Cross-Functionally at the Executive Level
• Manage Risk, Investment, and Efficiency
• Mentor engineering managers and senior technical leaders to build a strong leadership team.
• Drive Execution Excellence at Scale by establishing metrics and KPIs for engineering performance, reliability, and productivity.

Required Qualifications

• 15+ years of experience in software engineering, with 5+ years leading multiple teams.
• Strong background in distributed systems, databases, or large-scale cloud platforms.
• Deep Experience in Cloud Platforms - AWS, GCP, Azure
• Hands-on familiarity with Data and AI infrastructure concepts such as:
  • Vector search and indexing
  • Retrieval Augmented Generation (RAG)
  • Embedding pipelines
  • LLM and inference systems
  • Agent architectures and memory systems
• Experience building and operating production SaaS services at scale.
• Deep understanding of performance engineering, reliability, and cost optimization in cloud environments.
• Experience in Go, Python or Java.