Manager, AI Engineering - AI & Business Tech Engineering

DigitalOcean is at the start of a company-wide transformation to operate as an AI-native business, and this role helps define what that looks like. As Manager, AI Engineering you will lead a new team within the AI & Business Technology Engineering organization, inheriting a small nucleus of experienced AI engineers and growing it into a high-performing group of 6 to 8. This is a player-coach role: you set the technical bar, contribute to architecture and prototypes for agents, copilots, and the internal AI platform that powers them, and shape how AI engineering is practiced across the company. The work spans internal AI copilots for non-engineering teams, the platform and tooling beneath them (MCP gateway, agent runtimes, evaluation harnesses, model access), and re-architecting business processes across the Workday, Salesforce, NetSuite, and Greenhouse footprint to be AI-native from the ground up.

Key Responsibilities:

• Lead, mentor, and grow a distributed team of AI engineers building copilots, agents, and the internal AI platform that powers them.
• Act as a player-coach: review architecture, contribute to design and prototypes for critical agents and platform components, and write code where the team's leverage demands it.
• Design and deliver agentic systems end to end: orchestration, tool use, capability boundaries, memory and state, evaluation, observability, runtime governance, and incident response for non-deterministic systems.
• Build and evolve the internal AI platform, including the MCP gateway, agent runtimes, model access and routing, evaluation harnesses, and self-service developer experience.
• Partner with leaders across Finance, People, Sales, Marketing, and Security systems to identify the highest-leverage AI opportunities and ship them.
• Champion modern AI engineering practices: evaluation-first development, prompt and agent versioning, runtime guardrails, audit logging, human-in-the-loop escalation, and cost attribution for LLM workloads.
• Develop OKRs, instrument the right business and engineering metrics, and report progress clearly to leadership.

Qualifications:

• Significant experience as a software engineering manager with a track record of leading and growing teams that ship reliably in production.
• Hands-on depth in modern AI/ML systems: large language models, retrieval-augmented generation, agents and tool use, evaluation, and the operational discipline of LLMOps.
• Practical experience building or operating agentic systems, including orchestration frameworks (LangGraph, AutoGen, CrewAI, or equivalents), Model Context Protocol tooling, vector stores, and runtime guardrails.
• Experience designing internal developer platforms or productivity tooling that engineers actually choose to adopt.
• A clear point of view on AI governance and safety: audit logging, capability boundaries, minimum-privilege tool access, and alignment with frameworks like the NIST AI RMF.
• Strong software engineering fundamentals in at least one production language (Python, Go, TypeScript, or Java) and modern cloud-native infrastructure.