Head of AI Ops

🧡 Coverflex

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⚙️ TL;DR (The Essentials)

Role: Head of AI Ops
Seniority Level: Senior / Head of
Type: Leadership of a small central team while staying hands-on
Reports to: Rui Carvalho, COO
Languages: English (main) / Portuguese, Spanish or Italian a plus

Main Tools:

→ Claude (Projects, Skills, Plugins) and Notion AI agents
→ n8n / Make or similar orchestration, plus MCP connectors
→ SQL, Claude Analytics and Datadog for usage, cost and impact measurement
→ Scripting (TypeScript or Python) helps, because you should be able to build things yourself

Location: Remote (Europe only)

Compensation:

• Base Salary: 80K to 100K gross annually

• Equity: Yes – VSOPs

• Benefits: See below

• Contract Type: Permanent

💥 Your Impact

Coverflex is roughly 300 people and we spend over €70K a month on AI. Support, Finance, Legal, Ops, Insurance, RevOps and People all build their own agents, skills and automations. We have a plugin registry, a shared skill library and more and more automations that real business processes depend on.

The building works, and people everywhere are shipping. What's missing is someone who owns the whole picture: what we spend, what it returns, what is safe, and what one team built that others could reuse.

Today that sits informally with one person, on top of a full product job, and it deserves a lot more attention.

You lead a small team and decide where it goes. Teams keep their own AI work and pull AI Ops in when they need it, so when two teams want the same people at the same time, you choose. AI Ops doesn't approve anyone's work. It joins a team for a few days, a week or more, builds with them, and hands it back when it's solid. It also looks across everything we build with AI, so other teams can reuse what works.

You own the investment case too: what we spend on AI, what it returns, where we waste it and what we stop doing. When management asks about any of it, you're the one who answers, with evidence.

You'll know you're successful when, after 90 days, you've…

• Built the first honest picture of AI at Coverflex: spend by tool and team, what runs in production, who depends on it and what it returns

• Set a priority order across departments and held it, including a "not yet" to at least one senior stakeholder, with a reason they accept

• Got something live in at least three departments that the department itself now runs

• Made what already exists findable and reusable, so the second team to need something doesn't rebuild it

• Agreed a written governance line with Engineering, Security and Legal: what needs a guardrail, what needs review, and what teams can do freely

How we'll measure success:

• Workflows that changed for good across departments (we don't measure adoption rates or licence counts here)

• Return on AI spend, with evidence: time saved, cost avoided and better quality

• Reuse: share of new department AI work that starts from an existing pattern instead of from scratch

• Coverage of business-critical automations that have a named owner, a defined expected behaviour and an alert path

• Time from "a department asks" to "someone is working with them", because this team can't become a queue

🎯 What This Role Is Not

→ You won't own the customer-facing AI product. AI Product builds CoverflexAI, with its own roadmap. AI Ops works in tandem with them, so you exchange research, patterns and reusable logic.

→ You won't own infrastructure, hosting, SSO or data access. That's Engineering, DevX and Data.

→ You won't approve other teams' work. Departments own what they build, and nobody needs your OK to build. If teams start waiting on you, the setup isn't working. When something is unsafe or built the wrong way, you flag it early to the people who can act on it.

→ You won't only train people. Teaching is part of it, and sometimes showing a team how to do something is the best use of a week. What we look at is whether the process works better and is safer afterwards.

→ You won't only set strategy. It's a small team, so you build, sit with teams and use the tools yourself.

⚡ Reality Check - What Makes This Role Hard

→ No department reports to you. Each one owns its AI work and its budget line, so you get adoption by being useful to them.

→ You inherit what's already there. Prompts nobody documented, automations with no owner, tools bought by whoever needed them, and real business processes that depend on all of it.

→ Requests will outnumber the team from day one. Legal, RevOps, Finance and Support all have a real case, so you'll have to choose, and some of those conversations are uncomfortable.

→ People watch the spend. At over €70K a month, you'll have to justify it to people who want to see what it returns.

→ High usage doesn't mean impact. Almost everyone can be using AI while nothing that matters changes. We expect you to say so, even when the usage numbers would make you look good.

→ Each person in your team knows their part better than you. One knows how the areas work, one knows how we structure the infrastructure, and the AI Automation Engineer goes deeper into each team than you will. You set the direction and make the calls.

→ AI Ops is new. We drew the lines with AI Product, DevX and each department's own AI hires, but nobody has tested them yet, so you'll spend real time on where things sit.

👤 You

Must-haves (evidence, not years)

→ You've led AI or automation adoption across a whole company, beyond one team, and can show what changed because of it

→ You build with AI tools yourself (agents, skills, prompts, orchestration platforms), and other people use what you built

→ You've owned a budget or an investment case and defended it to executives with evidence

→ You've set priorities across departments that don't report to you, and made them stick

→ You know AI governance and risk well enough to draw the line and hold it: data handling, access, vendor review and audit trails

→ You measure: a baseline before you change something, and proof afterwards

→ You've worked in fast scale-ups and remote-first teams, with a lot of autonomy

Nice-to-have

→ Line management of a small, senior, mixed-discipline team

→ Background in solutions engineering, forward-deployed engineering, internal tooling, revenue/business operations or consulting

→ Familiarity with the EU AI Act in an employment and financial-services context

→ Experience in fintech or a regulated environment

🧬 Your DNA

Curious about how other people's work gets done, more than about models. Fine being the person who knows least about a domain, and asking the obvious question anyway. Doesn't trust a number that only looks good. Says "we're not doing that" early, before it rots in a backlog. Likes building, including the safety net. Can tell real risk from theoretical risk, and doesn't add process for its own sake.

Who will probably find this frustrating…

Someone who wants a big team and a mandate to enforce. Someone who wants to build new AI systems from scratch, which is closer to what AI Product does. Anyone who needs a stable backlog, a fixed scope or a clean org chart. Anyone whose first move in a governance conversation is to add a form.

👥 Manager & Team

Meet Your Manager

Hiring Manager: Rui Carvalho, COO
Location: Portugal
LinkedIn Profile:Profile Snapshot:Energy: Co-founder and operator. Starts scrappy and validates fast.
Communication: Informal and to the point. Shares ideas while they're still exploratory, and makes sure the people closest to a change hear it first.
Feedback Style: Direct and early. You'll hear it as soon as something isn't aligned or isn't moving, and he'll ask for yours.

How to work with me - in the Manager's own words:

"I'm happy to jump in when you need me, but ideally I don't have to. For that to work, I need transparency on what you're doing and why, and to be aligned before things go out. When we start something we believe will have impact, I stay close at the beginning so we can decide quickly on the risk of launching and move ahead. I care most about clear ownership and hitting what we set out to do. I want to know the impact and make sure we're going after what matters. I also use these tools myself, so you can go into the details with me."

Your Team

You'll set the priorities for:

• Francisco Roza, on the business side: builds and improves agents, and knows how the areas work

• An AI Automation Engineer (open role, separate posting), who goes deep with one team at a time: breaks the problem down, sets up the process and builds it

• Fábio Domingues, on the technical side: looks at how we structure the infrastructure, and is the bridge to Engineering

Key Stakeholders:

• Rui Carvalho, COO, your manager and the executive sponsor of AI Ops

• Martinho Aragão, Head of Product, who runs AI Ops together with Rui until you join and then hands it over

• João Caxaria, Head of AI Engineering in AI Product, which builds CoverflexAI and works in tandem with AI Ops

• Bruno Oliveira, VP Engineering, and the DevX team

• Department leaders across every function

💜 Access & Belonging (Equal Opportunity)

We hire for impact and potential, not pedigree.

We welcome applications from people with non-linear careers, career breaks, caregiving gaps, and those changing fields.

No discrimination on the basis of age, disability, gender identity/expression, marital or family status, pregnancy, neurodivergence, race/ethnicity, religion/belief, sexual orientation, or any other protected ground.

Assessment fairness:

We anchor on evidence of outcomes (what you shipped, moved, or influenced).

We actively de-bias by using structured rubrics, multiple assessors, and blind screening most of the time (we won't know your name, gender, or personal info until the interview stage).

📬 Application Clarity

No cover letter required.

Apply with your LinkedIn or upload your CV.

You may be asked a few short, relevant questions.

Total candidate time investment: ~9 hours end-to-end, including ~4 hours on the case.

🧩 Hiring Stages (What to Expect, Why & How Long)

Depending on holidays, we might run some stages in a different order.

1. CV / LinkedIn Screen — Signal check vs must-haves
You'll hear from us within 7 business days.2. Hiring Manager Interview (COO) - Mandate, judgement and how you operate without authority • 45–60 min

3. Behavioural Interview - With People • 30–45 min

4. Stakeholder Conversation - With two or three of the people you'd work with: AI Product, Engineering and a department lead • 45–60 min
This role only works through people who don't report to you, so they get a say. It's a two-way conversation: they're judging whether they'd pull you in, and you're finding out what you'd be walking into.

5. Case / Work Sample - Your 90-day AI strategy for Coverflex • about four hours to prepare, then 90 min with three of us

6. Final Conversation (CEO / C-Level) — Values, strategy and growth • 30–45 min

⏱️ Speed & Communication

• Decision: within 4 weeks of your application.

• Updates: weekly if the process runs longer.

• Scheduling: interviews between 9:00 and 18:00 CET (flexible across Europe).

• Feedback: from the Case stage onwards, you'll always receive written or verbal feedback - what went well, and what to strengthen next time.