AI Operations Lead

📋 Description

• • As the AI Operations Lead at Honeycomb Inc., you will serve as a senior individual contributor responsible for transforming Honeycomb into an AI-superpowered organization by building, deploying, and evangelizing internal AI technology that is reliable, high-leverage, and widely adopted across engineering, go-to-market, and business operations.
• • You will own the end-to-end strategy for Honeycomb’s internal AI platform, partnering with Engineering Enablement, Data Engineering, security, IT, and departmental leaders to architect scalable systems, automate critical workflows, and drive organization-wide AI fluency through enablement, governance, and trusted guidance.
• • Honeycomb is a fully distributed, mission-driven company of over 200 employees known for its innovative observability platform, strong engineering culture, and commitment to inclusivity and autonomy—backed by Series D funding and recognized as one of Forbes’ America’s Best Startups in 2022 and 2023.
• • In this role, you will deepen your expertise in AI platform architecture, cross-functional leadership, and responsible AI deployment while shaping how a fast-growing tech company leverages AI to multiply impact, improve decision-making, and establish best practices in safety, evaluation, and adoption.
• • 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 of 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.