Staff AI Operations Lead

AI has changed what's possible in go-to-market, support, and business operations. The constraint is no longer "can we build it" — it's "can we deploy it into a real workflow, prove it made things better, and keep it working."

Most companies are stuck at the demo stage: a lot of clever prompting, very little that runs reliably on Tuesday morning without someone watching it. This role exists to get us past that, everywhere outside of engineering.

You'll sit on the Data team, which is where this work belongs. Our team already operates at the seam between the business and its systems — we partner daily with RevOps, Customer Success, Sales, and Product, and we own the data infrastructure these workflows depend on. AI-in-the-business is a data problem with a model attached, and you'll have the pipelines, the warehouse, and the domain relationships already in place.

You are a senior individual contributor. Your job is to take business workflows that are slow, manual, or inconsistent, and make them fast, automated, and measurably better — then do it again, and leave behind enough shared scaffolding that the third one is easier than the first.

• Partner with department leaders to identify the highest-leverage workflows in their orgs, and go deep enough on how the work actually happens to know where a model helps and where it doesn't.
• Define success before you build: cycle time, quality, cost, risk. Instrument it. Report on it honestly, including when the answer is "this didn't work."
• Deliver working systems, not prototypes, with evaluation harnesses, human-in-the-loop review where the stakes call for it, and ongoing monitoring.
• Iterate on adoption the way a product manager would: user research, documentation, and a real plan for getting people to use the thing.

• Establish patterns for model access, orchestration and agents, prompt and tool management, evaluation, logging, and cost control — derived from what real workflows needed, not designed in advance.
• Build on the Data team's existing infrastructure rather than standing up parallel systems.
• Co-own shared components with your counterpart in Engineering; identify dependencies early and bring them into partner roadmaps collaboratively.

• Create templates, playbooks, and golden paths: prompt patterns, agent patterns, safety checklists, evaluation patterns.
• Run office hours, demos, and community-of-practice sessions that make adoption feel accessible rather than intimidating.
• Develop enablement material for distinct audiences — GTM, operations, leadership — from fundamentals through advanced workflows.
• Help people build judgment about AI: when to use it, when not to, and how to tell whether it worked.

• Partner with Security, IT, and Legal on access control, data handling, vendor risk, and policy — so that employees have a clear, fast, safe path to experiment.
• Work with the Data team to apply our data classification standards to AI use cases: what data can be used for retrieval and context, and what audit trails are required when sensitive data flows through LLM pipelines.

• Deep experience shipping systems into real business workflows — you've owned something end to end, in production, that non-technical people depended on daily.
• Hands-on fluency with modern AI stacks: LLMs, retrieval, tool use and agents, evaluation, guardrails — plus the pragmatism to keep things shippable.
• The ability to learn someone else's job well enough to improve it. This role lives or dies on understanding how support, sales, or finance actually work.
• A track record of leading cross-functional initiatives without formal authority, bringing clarity, momentum, and crisp decision-making.
• Comfort navigating security, privacy, and compliance tradeoffs in real organizations: data classification, retention, access controls, vendor risk.
• Strong teaching instincts — you explain complex things simply, write clear docs, and meet people where they are.
• Demonstrated ability to stay current as the landscape shifts, and to change your mind in public when the evidence does.

• SQL fluency and comfort in a modern data stack (dbt, a cloud warehouse, BI tooling).
• Experience with workflow automation or enterprise systems: case management, ticketing, CRM, knowledge bases.
• Familiarity with observability practices for AI systems: monitoring, quality signals, evaluation pipelines, cost and latency analysis.
• Change management experience across a distributed, multi-function company.
• Interest in external evangelism — writing, speaking, podcasts, peer groups.