Manager, AI Engineering - AI & Business Tech Engineering
$180,000–$200,000 year
Remote · Boston, Massachusetts, United States
Job Summary
Manager, AI Engineering at DigitalOcean leads and grows a distributed AI engineering team tasked with building copilots, agents, and the internal AI platform that powers AI-native workflows across the company. The role is a player-coach in architecture reviews, prototyping, and code, while shaping the technical roadmap, partnering with finance, people, sales, marketing, and other business functions to re-engineer processes and deliver measurable AI outcomes. Responsibilities include mentoring a 6–8 person team, delivering end-to-end agentic systems (orchestration, memory/state, evaluation, observability, runtime governance), evolving the internal AI platform (MCP gateway, agent runtimes, model access), collaborating with cross-functional leaders to identify opportunities, ensuring governance and safety, developing OKRs and metrics, recruiting and developing top AI engineering talent, and contributing to leadership planning. Compensation range is $180,000–$200,000 and this is a remote role (Boston-area hint present). Include experience with modern AI/ML systems, agentic tooling, and enterprise integration using Workday/Salesforce/NetSuite/Greenhouse as bonuses.
Required Qualifications
- Significant experience as a software engineering manager
- Hands-on engineering depth in modern AI/ML systems (LLMs, retrieval-augmented generation)
- Experience building or operating agentic systems (orchestration frameworks, MCP tooling, vector stores)
- Experience designing internal developer platforms or productivity tooling with self-service APIs and SDKs
- Clear point of view on AI governance and safety (audit logging, human-in-the-loop, minimum-privilege access)
- Strong software engineering fundamentals in Python, Go, TypeScript, or Java; cloud-native infra (Kubernetes, serverless, gRPC)
- Ability to design and ship AI-native transformations with non-engineering business teams
- Excellent written and verbal communication; ability to influence non-engineering stakeholders
- Experience hiring and developing AI engineering talent; hybrid-work capability
- Bonus: experience re-engineering enterprise processes (Workday, Salesforce, NetSuite, Greenhouse) or deploying AI tooling at scale (Cursor, Claude Code, GitHub Copilot)
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