[Remote] Manager, AI Engineering - AI & Business Tech Engineering

Note: The job is a remote job and is open to candidates in USA. DigitalOcean is a cutting-edge technology company focused on simplifying cloud and AI for builders. They are seeking a Manager, AI Engineering to lead a new team within their AI & Business Technology Engineering organization, driving the development of AI-native capabilities across various teams.

Responsibilities

• 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)

Skills

• 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)

Benefits

• Reimbursement for relevant conferences, training, and education
• All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development
• Employee Assistance Program
• Local Employee Meetups
• Flexible time off policy
• You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance
• Equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program

Company Overview

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