Senior AI Enablement Engineer

The IT and Security team sits within the Office of the CTO (OCTO) at CloudZero and runs IT, data, governance, risk and compliance, and product security, and leads the Claude platform rollout, AI landing zones, Okta as code, and AI cost and usage reporting. We are hiring a Senior AI Enablement Engineer. This is a technical role on the IT and Security team, but it is not traditional IT. The job is to help every team at CloudZero do more with AI, and to make sure it can be done safely. The role involves embedding with teams such as Finance, Sales, Marketing, Customer Success, People, and Engineering, redesigning their work with AI, building agents, automations, and tools, and teaching people to build their own. This is one of three related senior roles on the team, each a single hire. This posting is for one opening.

Responsibilities

Partner with business teams to map their workflows, find friction, and agree on AI enablement goals and success metrics before building; Design, build, and run AI-powered workflows, agents, and automations, from quick wins to production systems; Integrate AI with company systems (CRM, finance, HR, support, collaboration, and data platforms) using APIs, MCP servers, iPaaS tools, and custom code; Turn solutions built for one team into reusable patterns, skills, or templates; Identify platform gaps for deploying and governing AI safely (identity and access, data access and classification, logging, evaluation, human review, spend controls), then build or buy and implement them; Own the build vs buy decision for AI tooling, including vendor evaluation, trials, and recommendations with cost and risk tradeoffs; Work with Security, G&A, and Engineering to write and maintain AI usage and data handling standards; Set up evaluations and monitoring so shipped workflows keep working as models and systems change; Track AI adoption, outcomes, and spend across the company and report to IT leadership; Raise AI fluency through office hours, working sessions, playbooks, and one-on-one coaching; Document everything built so the work is repeatable and scalable; Build the experience for internal agent builders, including templates, deploy paths, docs, and office hours; Turn repeated requests into self-serve tools and data products, working with the IT & AI Systems Engineer on systems and employee lifecycle automation; Partner with IT and data platform engineers so every agent runs with the permissions of the person who invokes it, with no shared service accounts, and with its cost attributed to a team

Requirements

5+ years in a technical role such as software engineering, IT or systems engineering, solutions/sales engineering, automation, or internal tools; Hands-on coding skills in Python, TypeScript, or similar, plus solid SQL and comfort working with REST APIs and webhooks; Deep personal use of modern AI tools, including coding agents, LLM APIs, MCP, agent skills or custom GPTs, and retrieval patterns; Ability to enable and train, with confidence facilitating workshops and trainings in an engaging manner; A track record of building tools, automations, or AI workflows that changed how a real team works, with specific examples and results; Working knowledge of identity and access (SSO, SCIM, OAuth scopes, service accounts, least privilege) and how data permissions carry into AI tools; The habit of defining what good output looks like, testing it, and owning the result; Strong business sense and the ability to earn trust with non-technical teams quickly; Clear writing and teaching, able to explain technical ideas to technical and non-technical audiences; Comfort with ambiguity; Uses Claude Code, Claude Desktop, or Cursor before solving a problem by hand, and can discuss which models and tools are good at what, where they fall short, and how to prompt them well

Nice to have

Experience as a forward deployed engineer, solutions engineer, or consultant who had to deliver results inside someone else's organization; Experience with iPaaS or workflow tools (for example Workato, Zapier, n8n, or Make) and enterprise AI platforms; Familiarity with AI governance frameworks such as the NIST AI Risk Management Framework, or with SOC 2 style security programs; Experience with data warehouses, data pipelines, and retrieval systems; Background in cloud infrastructure, cloud cost management, or SaaS spend management; Experience leading change across skeptical teams or building internal training programs that stuck