AI Operations Lead

What We’re BuildingHoneycomb is a service for the near and present future, defining observability and raising expectations of what developer tools can do! We’re working with well known companies like HelloFresh, Slack, LaunchDarkly, and Vanguard and more across a range of industries. This is an exciting time in our trajectory, we’ve closed Series D funding, scaled past the 200-person mark, and were named to Forbes’ America’s Best Startups of 2022 and 2023! If you want to see what we’ve been up to, please check out these blog posts and Honeycomb.io press releases. Who We AreWe come for the impact, and stay for the culture! We’re a talented, opinionated, passionate, fiercely inclusive, and responsible group of bees. We have conviction and we strive to live our values every day. We want our people to do what they truly love amongst a team of highly talented (but humble) peers.How We WorkWe are a fully distributed company, which means we believe it is not where you sit, but how you deliver that matters most. We invest in our people and care about how you orient to our culture and processes. At the same time we imbue a lot of trust, autonomy, and accountability from Day 1. About the problem spaceAI 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 roleAs 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.ResponsibilitiesOwn 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 pipelinesWork 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 conceptsCreate 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 c