Senior AI Enablement Engineer

CloudZero•Boston, MA

1d•$150,000 - $205,000

About The Position

The Senior AI Enablement Engineer is a technical role on CloudZero’s IT and Security team within the Office of the CTO. The role focuses on helping teams across the company redesign workflows with AI and ensuring those solutions can be deployed safely. The engineer embeds with business teams to understand how work is done, builds AI-powered workflows, agents, automations, and tools, and teaches employees to build their own. They also identify and implement foundational capabilities such as access controls, data connections, logging, evaluation, and cost tracking. This role complements the Senior IT & AI Systems Engineer, who owns core IT systems, identity, and employee lifecycle automation, and the Senior AI Data & Platform Engineer, who builds the data, retrieval, APIs, and agent runtime. The Senior AI Enablement Engineer focuses on people and workflows, shipping solutions on top of that foundation. The team works automation-first, instruments systems for cost, and aims to address recurring requests by fixing upstream causes and building reusable or self-service capabilities. CloudZero describes itself as the AI ROI Company. Its financial control plane helps finance, IT, and engineering connect AI spending to outcomes across providers in real time. The company reports processing 14 trillion billing events in the last twelve months, being the first listed partner on Anthropic’s cost and usage API, and raising over $119 million, including a $56 million Series C. CloudZero emphasizes a collaborative, fast-moving environment that values ownership, creativity, and curiosity.

Requirements

At least 5 years of experience 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 a similar language; solid SQL skills; and comfort 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 show how that use has changed over the last year.
Ability to enable and train others, including confidence facilitating engaging workshops and trainings that foster continuous learning and development.
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 concepts such as SSO, SCIM, OAuth scopes, service accounts, and least privilege, including how data permissions carry into AI tools.
Habit of defining desired output, testing it, and owning the result.
Strong business sense and ability to quickly earn trust with non-technical teams.
Clear writing and teaching skills, including explaining technical ideas to technical and non-technical audiences.
Comfort working with ambiguity and determining where to start without a detailed roadmap.
Regular use of Claude Code, Claude Desktop, or Cursor to solve problems, and credible knowledge of model and tool strengths, limitations, and effective prompting.

Nice To Haves

Experience as a forward deployed engineer, solutions engineer, or consultant delivering results inside another organization.
Experience with iPaaS or workflow tools such as 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 were adopted.

Responsibilities

Partner with business teams to map workflows, identify friction, and agree on AI enablement goals and success metrics before building.
Design, build, and operate AI-powered workflows, agents, and automations, from quick wins to production systems.
Integrate AI with company CRM, finance, HR, support, collaboration, and data platforms using APIs, MCP servers, iPaaS tools, and custom code.
Identify solutions that can benefit multiple teams and turn them into reusable patterns, skills, or templates.
Identify gaps in safe AI deployment and governance capabilities, including identity and access, data access and classification, logging, evaluation, human review, and spend controls; build or select and implement solutions.
Own build-versus-buy decisions for AI tooling by evaluating vendors, running trials, and making recommendations that address cost and risk tradeoffs.
Work with Security, G&A, and Engineering to create and maintain practical AI usage and data handling standards, and make the approved path easy to use.
Set up evaluations and monitoring to ensure shipped workflows continue working as models and systems change.
Track company-wide AI adoption, outcomes, and spend, report results to IT leadership, and use CloudZero to understand the results of internal AI spend.
Increase AI fluency through office hours, working sessions, playbooks, and one-on-one coaching.
Document work so it can be repeated and scaled.
Build internal agent-builder experiences, including templates, deployment paths, documentation, and office hours.
Turn repeated requests into self-service tools and data products, working with the IT & AI Systems Engineer on the systems and employee lifecycle automation agents depend on.
Partner with IT and data platform engineers to ensure agents use the invoking person’s permissions without exceeding them, avoid shared service accounts, and attribute costs to a team.