AI Ops Engineer (Business Operations)

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, writing off, or defaulting on 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 Kernel 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 Notion already use Kernel on their own systems. Agents are next, and there will be far more of them.

We have raised $20M from top VCs and operators at Plaid, OpenAI, Slack and others, and we are growing 10x YoY.

The Role

We’re looking for an AI Ops Engineer to build the internal systems that help Kernel scale without adding unnecessary manual work. You’ll work across GTM, Marketing, Customer Success and Operations - 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.

You’ll report to Arvin Ashrafi, Head of Finance & Operations, and partner closely with GTM, Marketing, and Customer Success leadership.

What You’ll Be Doing

• Define how an AI-native company operates: help build Kernel’s internal playbook for using AI across GTM, Marketing, Customer Success and Operations

• Make our GTM team more productive: automate the repetitive work in prospecting, account research, meeting prep and CRM hygiene, so BDRs, AEs and Account Managers spend more time with customers

• Build internal tools and workflows: use coding agents, APIs, automation platforms, no-code tools or lightweight code

• Turn company data into operational leverage: connect and improve data across our systems, then put it to work in AI agents, proactive workflows and better decision-making

• Put AI agents to work across Kernel: give teams agents with the right context, tools and guardrails so they can move faster without sacrificing judgement or reliability

• Be Kernel’s own internal customer: deploy Kernel on Kernel’s CRM, improve our GTM data and turn what you learn into useful feedback for Product and Engineering

• Level up the team: help colleagues become confident AI users, document what works and turn successful experiments into repeatable ways of working

Your First 6 Months

For your first six months, you’ll focus on our sales team. You’ll sit with our BDRs, AEs and Account Managers and learn how they work day to day. Then you’ll automate everything that doesn’t need a human. That means going deep on Salesforce and our GTM stack, and owning the RevOps workflows behind them. After that, you’ll bring the same approach to Marketing, Customer Success and Operations.

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

• Experience inside a high-growth startup, or building your own, is highly desirable

• Curious about sales: you’re keen to learn how our BDRs and AEs work, and how Salesforce is set up, before you build for them

• 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

• Strong data instincts: you can turn fragmented, messy information into reliable workflows and create feedback loops that keep it trustworthy

• Can’t unsee inefficiency: you see the company as a connected system; when you find a broken or unnecessarily manual process, your instinct is to understand it, build the fix and make sure it sticks

This role may not be for you if you:

• Prefer deep specialization over breadth: this role means switching between projects and teams, depending on business needs

• Prefer steady-state work over project-based sprints: priorities will shift as the business evolves

• Only want “strategic” work: you will personally build the tools, clean the data and fix the workflows

This role is definitely not for you if you:

• Need every task to be clearly scoped before starting: ambiguity and learning on the fly are constant

• Avoid operational grunt work or lose interest after the prototype

• Want to protect a 9-to-6 or a mostly remote setup: we are in the office together 4-5 days a week and the pace is high

What We Offer

We will do our best to offer you a ride of a lifetime. It will not be easy, but it will be thrilling.

• Salary: £60,000-£85,000 + equity

• 24 days holiday per year + bank holidays

• £450 monthly office dinner allowance

• 2 weeks work-from-anywhere

• Generous parental leave policy

• Pension plan

• Top-spec equipment and central London office

• Team events and dinners

• Work directly with the founders to deploy AI across a fast-growing company

• High-autonomy, high-trust environment with a small team shipping at pace

Team

Arvin Ashrafi, Head of Finance & Operations

Amin Shafi, BizOps Associate

Dave Bentham, Head of Sales

Visas & Relocation

Not offered for this role.

Interview Process

• Stage 1: Screening - Intro call

• Stage 2: Technical - In-person case interview

• Stage 3: Coffee chat - In-person discussion with members of our GTM team

• Final Stage: Values - Interview with founders for culture and values alignment

If there is mutual fit, we move to references and offer.