AI Operations Lead
Confirmed live in the last 24 hours
Honeycomb
Job Description
About the problem space
AI is changing how work gets done across engineering, go-to-market, and business operations. The constraint is no longer “can we build it,” but “can we deploy it safely, repeatably, and in ways that measurably improve outcomes.”
The charter of this role is to make Honeycomb an AI-superpowered company through building/buying, deploying, and evangelizing AI technology. This role exists to make AI at Honeycomb:
- Reliable and ubiquitous (platform, architecture, governance)
- High-leverage (automating real workflows; helping employees 2x their impact)
- Widely adopted (education, enablement, and standards)
What you’ll do in the role
As the AI Operations Lead, you’ll be a senior individual contributor who operates across the company to scale how we build and use AI internally. You’ll partner with our Engineering Enablement and Data Engineering teams on the technical and operational strategy for our internal AI platform, work closely with teams around the company to automate key business flows, and raise the company’s AI fluency through hands-on enablement and clear guidance.
This is a role for someone who can move between architecture reviews, workflow design, vendor evaluation, security/privacy constraints, documentation crafting, and practical enablement, while keeping momentum and trust across many stakeholders.
Responsibilities
- Own our internal, company-wide AI strategy, building a roadmap that balances quick wins with foundational platform investments.
- Supervise the architecture of our internal AI platform, guiding our Engineering Enablement and Data Engineering teams to find the right approach for both Engineering and company-wide AI platform capabilities.
- Support the creation reference architecture for internal AI capabilities: model access, orchestration/agents, prompt and tool management, evaluation, logging/telemetry, and cost controls.
- Partner with engineering and data to ensure AI platform components are built on shared infrastructure rather than point solutions. Identify AI workload dependencies early and bring those requirements into partner roadmaps collaboratively.
- Partner with security/IT/engineering/data on access control, data handling, vendor risk, and policy implementation, making sure that employees have a clear, safe path to experiment and move quickly with AI technologies.
- Partner with data, analytics, and security to establish shared data classification standards for AI use cases — what data can be used for retrieval or context, and what audit trails are needed when s
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