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 sensitive data flows through LLM pipelines

Work with engineers and departmental SMEs to automate key business flows with AI across the company:

• Identify the highest-leverage internal workflows to automate (e.g., support triage, sales/CS enablement, incident follow-ups, internal knowledge retrieval, finance ops, talent ops).
• Lead cross-functional discovery to define success metrics (cycle time, quality, cost, risk) and then deliver end-to-end solutions.
• Build lightweight product thinking around internal tools: user research, iteration loops, documentation, and adoption plans.
• Establish measurement: evaluation harnesses, human-in-the-loop review where appropriate, and ongoing performance monitoring.

Evangelize and educate our employee base on AI tooling and core concepts

• Create templates, playbooks, and “golden paths” (prompt patterns, agent patterns, safety checklists, skills, evaluation patterns).
• Run office hours, internal demos, and community-of-practice sessions that make adoption feel accessible and safe.
• Develop and deliver enablement resources/training for different audiences (engineering, GTM, operations, leadership), from fundamentals to advanced workflows.
• Build great relationships with leaders around the company, helping to make AI at Honeycomb feel truly company-wide, not just siloed into department-specific tools.
• Help teams build good judgment about AI: when to use it, when not to, and how to validate outcomes.
  
  

What you’ll bring to the role

• Proven ability to stay current on the state of AI technology, evolving your perspective and approaches as the landscape changes.
• Significant experience as a senior IC building and operating production-grade platforms or internal systems with high trust requirements.
• Experience architecting modern AI stacks (LLMs, RAG, tool use/agents, evaluation, guardrails), combined with the judgment and pragmatism to keep things shippable.
• Track record of leading cross-functional initiatives with and 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).
• Ability to teach and influence: you can explain complex topics simply, write clear docs, and meet people where they are.
  
  

Nice to have

• Experience building internal developer platforms, workflow automation platforms, or enterprise automation (e.g., case management, ticketing, CRM, knowledge systems).
• Familiarity with observability practices for AI systems (monitoring, quality signals, evaluation pipelines, cost/latency analysis).
• Experience with change management: enabling adoption across a distributed company with multiple functions and priorities.
• Experience with and interest in external evangelism: writing, speaking, podcasts, social media, external peer groups, etc.
• Experience navigating data governance and security: data classification, RBAC strategy, acceptable use policies for LLM inputs and outputs, audit logging and working closely with data and engineering to define and enforce standards collaboratively.