AI Ops Engineer (Product)

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About the role

About Kernel Agents are starting to sell, buy and operate on behalf of companies. But before an agent can close a deal, qualify an account, route a lead or assess risk, it has to answer a basic question: which company is this? Today, that answer is messy, and no one answers it reliably. Business identity lives across CRMs, ERPs and third-party datasets full of duplicates, missing parent companies, stale addresses and incorrect enrichment. The cost shows up across the enterprise: sales teams miss revenue opportunities because they lack the right account context; operations teams take on avoidable exposure because risk signals are fragmented or wrong; and finance teams end up chasing and writing off bad debt that should have been caught earlier. Humans have worked around that mess for years. Agents cannot. They need a reliable business identity layer they can trust. Kernel is building that layer: the business registry for agents. We issue a permanent KERN ID for every business, plus the context an agent needs to act on it. Ops, data and revenue teams at Gong, Legora, Mistral, Canva and Checkout already use Kernel on their own systems. Agents are next, and there will be far more of them. We have raised $14M from top VCs and operators at Plaid, OpenAI, Slack and others, and we are growing 5x YoY. The Role We’re looking for an AI Ops Engineer for our product team to build the internal systems that help Kernel scale without adding unnecessary manual work. You’ll work across Product, Customer Support, and Engineering - finding the highest-leverage problems and building practical AI-powered workflows. This is a hands-on builder role. You’ll take ideas from a vague business problem to a tool or workflow that people use, then maintain and improve it in production. You’ll choose the fastest sensible approach for each problem - coding agents, automation platforms, APIs, data pipelines or lightweight code. Your focus area will be whatever is the most critical blocker for Kernel’s growth, whether that’s automating our customer support, tying our data feedback loop to automatically seed our evals (benchmarks), building tooling for our sales team to demonstrate Kernel, and so on. You’ll report to Marcus Henglein, our co-founder who leads our Product, Engineering, and Client Delivery teams. What You’ll Be Doing • Automating customer support : The first focus area is to lead our initiative on automating as much of our technical customer support as possible, leveraging Kernel’s docs and MCPs, the right kind of AI tooling, and working closely with the engineering team. • Turning user feedback into evals: Build workflows that turn data issues reported by users into test cases, helping Kernel catch recurring errors and measure improvements. • Keeping docs current: Build workflows that detect product changes and keep our documentation accurate. • Making Kernel easier to demo: Build tools that help our sales team prepare and run demos using relevant account data. Priorities shift, problems often start loosely scoped, and you’ll own the work through maintenance. What You Bring • A track record of shipping : you’ve built tools, automations or AI workflows that people use every day, ideally inside a small, fast-moving startup. Around 2–5 years of experience is typical, but what you’ve built matters more than your years. Ex-founders welcome • Curious about product : you’re keen to learn how a product/engineering organization works in a fast-paced startup environment • AI-tool obsessed : you are constantly testing new models, agents, MCPs and workflows, and you have the judgement to turn them into reliable systems that people actually adopt - but without turning off the “Local LLM” that you were born with (your brain) • Strong data instinc

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