Manager, AI Engineering - AI & Business Tech Engineering
DigitalOcean
| Company | DigitalOcean |
| Category | Engineering |
| Location | Boston |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Manager |
| Salary | Not stated by the employer |
| Posted | 11 Jun 2026 |
| Last verified | 7 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. We are looking for a Manager, AI Engineering who is passionate about transforming the way a global company operates with AI at its core.
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.
What You’ll Do:
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