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As the Manager, AI Engineering at DigitalOcean, you will lead a new team within our AI & Business Technology Engineering organization, reporting to the Sr. Director of AI & Business Technology Engineering. You will inherit a small, experienced nucleus of AI engineers and grow the team to deliver AI-native capabilities that change how DigitalOcean's teams-Engineering, Finance, People, Sales, Marketing, Support, and IT-get work done.

This is a player-coach role. You will 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 DigitalOcean. You will also hire, coach, and grow a high-performing distributed team-while partnering deeply with business and functional leaders to turn ambitious ideas into shipped, measurable outcomes.

We are at the start of a company-wide transformation to operate as an AI-native business. You will help define what that looks like inside DigitalOcean. The work spans three connected pillars: building internal AI copilots and agents for non-engineering teams, evolving our internal AI platform and tooling (MCP gateway, agent runtimes, evaluation harnesses, model access, Cursor/Claude rollout), and re-architecting business processes across our Workday, Salesforce, NetSuite, and Greenhouse footprint to be AI-native from the ground up.

• Lead, mentor, and grow a distributed team of AI engineers (starting from an established nucleus, scaling to a high-performing group of 6-8) 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, write code where the team's leverage demands it, and set a high technical bar.
• Shape and execute the technical roadmap for the AI Engineering team in partnership with the Senior Director, AI & Business Technology Engineering-across internal AI copilots for teams, the AI platform and developer experience that supports them, and AI-native business process re-engineering across DigitalOcean.
• 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 our internal AI platform-including the MCP gateway, agent runtimes, model access and routing, evaluation harnesses, and self-service developer experience-so every DO engineer and business team has a paved path to building with AI safely.
• Partner with leaders from Finance & Supply Chain Systems, People Systems, Sales & Marketing Systems, Collaboration & Security Systems and their non-engineering business owners to identify the highest-leverage AI opportunities and ship them.
• Collaborate closely with peer leaders in Enterprise Architecture, Data Engineering, Program Management, and Security to ensure our AI systems are well-architected, governed, observable, and trusted.
• 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 for the team, instrument the right business and engineering metrics, and clearly report progress to leadership and the broader organization.
• Recruit world-class AI engineering talent in Boston, Cambridge, and broader US & non-US hubs; coach and develop the team you build; create an environment where engineers do the best work of their careers.
• Contribute to AI & Business Technology Engineering leadership team planning and goal-setting, represent the AI Engineering team's perspective in cross-org forums, and contribute back to internal communities of practice (agent-skills, Claude pilot, AI workflows).

• Adoption of AI copilots and agents by non-engineering teams across DigitalOcean (active users, workflows automated, hours returned).
• Reliability, latency, and unit economics of the internal AI platform (uptime, p95 latency, $/task, $/user/month for shared AI infrastructure).
• Quality and safety of agent behavior in production (eval scores, regression rate, incident rate, time-to-detect and time-to-contain for agent-related issues).
• Engineering team health (hiring velocity against the plan, retention, internal mobility, and engineer-reported leverage of platform tools).
• Business outcomes delivered against the AI-native transformation roadmap (named workflows re-engineered, cycle-time reductions, cost savings, and revenue enablement).

• Significant experience as a software engineering manager, with a strong track record of leading and growing engineering teams that ship reliably in production.
• Hands-on engineering depth in modern AI/ML systems: large language models, retrieval-augmented generation, agents and tool use, evaluation, and the operational discipline of LLMOps (prompt versioning, regression testing, cost attribution, observability for non-deterministic outputs).
• Practical experience building or operating agentic systems-orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or equivalents), Model Context Protocol (MCP) tooling, vector stores, and runtime guardrails.
• Experience designing internal developer platforms or productivity tooling that engineers actually choose to adopt, including golden paths, self-service APIs, and SDKs.
• A clear point of view on AI governance and safety: audit logging, capability boundaries, minimum-privilege tool access, human-in-the-loop escalation, 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 (Kubernetes, serverless, gRPC, observability stacks).
• A bias for shipping: integrating customer and stakeholder feedback into how the team works, focusing on outcomes over outputs, and unblocking the team with pragmatic decisions.
• Excellent written and verbal communication skills, with a demonstrated ability to influence non-engineering stakeholders and translate ambiguous business problems into well-scoped AI systems.
• Experience hiring and retaining strong AI engineering talent in competitive markets, and growing junior engineers into senior contributors.
• Comfort working in a hybrid environment-able to partner closely with our Boston/Cambridge community while leading distributed teammates across the US and beyond.
• Bonus: experience re-engineering business processes in enterprise systems (Workday, Salesforce, NetSuite, Greenhouse, or similar), or working closely with finance, people, GTM, or support functions on AI deployments.
• Bonus: prior experience deploying AI tooling at scale to internal users (Cursor, Claude Code, GitHub Copilot, or equivalent enterprise rollouts).

• $180,000-$200,000

*This is a remote role

JR: 2026-8007

#LI-Remote