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Manager, AI Engineering - AI & Business Tech Engineering

Job in Boston, Suffolk County, Massachusetts, 02298, USA
Listing for: DigitalOcean
Full Time position
Listed on 2026-06-26
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software)
Job Description & How to Apply Below

Dive in and do the best work of your career rney 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 Digital Ocean, 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 Digital Ocean’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 Digital Ocean. 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.

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 Digital Ocean.
  • 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).
Key Metrics
  • Adoption of AI copilots and agents by non‑engineering teams across Digital Ocean (active users, workflows automated, hours returned).
  • Reliability, latency, and unit economics of the internal AI platform (uptime,…
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