VP Engineering (hands-on) in Berlin

• VP Engineering — AI-Native B2B Trade Platform, Berlin
• Via Xcede — confidential search
• The company

A fast-growing, well-funded Berlin-based startup rebuilding B2B procurement from the ground up.

This category is a €900B+ market in Europe alone, still run almost entirely by people — four or five intermediaries between supplier and customer, with up to 40% of the margin evaporating along the way.

This company is building the alternative: they buy from global suppliers and sell directly to European customers on their own account, running sourcing, pricing, sales, logistics, credit, and financing through AI.

One request in, a firm quote back in minutes, delivered on time — no warehouses, no fleet, no inventory risk, every trade pre-sold before it’s bought.

They’re also the credit counterparty, scoring and insuring customers in minutes with working capital that’s bank-funded, not equity-funded.

Where they are today: strong triple-digit-million-euro turnover run rate, majority repeat customers, seven live European markets sourcing globally, 80+ people, and a recent well-backed funding round from top-tier European VCs plus bank working-capital facilities.

The role

As VP Engineering, you own the Engineering organization and turn the platform and AI vision into shipped systems.

You report to the CPO and partner closely with product, commercial, operations, and finance — leading Senior Product Engineers, Staff Engineers, and QA.

This is an execution mandate, not a caretaking one: you carve the core systems into a modern platform, build the AI agents into one holistic system running the trade lifecycle end-to-end, and scale the team that delivers it.

You inherit a working machine at full speed — the job is to rebuild it in flight without losing a day of momentum.

Year-one priorities: carving out the transaction core and bringing the AI agents into one orchestrating layer.

• What you’ll do
• AI & Automation
• Build the AI agents into one holistic, orchestrating layer across sourcing, pricing, quoting, sales, logistics, and financing.
• Drive full transaction-lifecycle automation; own automation-rate and no-touch-quote targets as first-class delivery KPIs.
• Work with the data team (sits with the CPO) on making every transaction make the system smarter.
• Stay at the practical frontier of agentic AI — ship what creates leverage now, skip the demo-ware.
• Put guardrails, evals, and human-in-the-loop controls in place wherever automation touches money or customers.
• Platform & Architecture
• Own execution of the target architecture — the system of record for the company’s trade platform.
• Lead the carve-out of the transaction core into a modern, modular light ERP, with a clear migration path and zero disruption to daily trading.
• Shape the role of Salesforce in the landscape — what stays, what moves to the new core, how the two work together.
• Make pragmatic build-vs-buy calls and keep technology cost per transaction on a falling curve.
• Make new-market launch a repeatable platform motion — geographies, currencies, languages, compliance, built in rather than bolted on.
• Keep the platform ready for technical due diligence at any moment.
• Engineering Organization & Talent
• Lead the engineering org: onsite talent in Berlin plus a nearshoring team you manage directly, not at arm’s length through a vendor.
• Build lean by default — raise output through AI across the engineering workflow rather than growing headcount. Lead as an AI-first engineering leader.
• Hire and develop senior engineering leaders, including 1-2 Staff Engineers reporting to you who own architecture across teams.
• Build a culture of ownership, speed, and craftsmanship — small teams, high leverage, no bureaucracy.
• Stay hands-on: review designs with your Staff Engineers, pair on the hardest problems, keep decisions close to the code.
• Enablement & Data Integrity
• Enable business teams to build and automate with AI safely — paved paths, standards, and review practices for non-engineers.
• Partner with the CPO on data integrity — one source of truth, clear access and quality controls.
• Make speed and safety one operating model, not a trade-off, and communicate that clearly across the company.
• Delivery & Cross-Functional Partnership
• Act as technical sparring partner to the CPO on architecture, roadmap feasibility, and sequencing.
• Translate the product roadmap into shipped software; turn commercial/operations needs into delivery.
• Support the CPO and CEO in technical due diligence with a clear, numbers-backed view of architecture, data, and AI.
• What success looks like

Engineering operates with clear ownership and no loss of delivery speed.

The transaction core runs on a modern, modular platform — carve-out live, migration executed.

One orchestrating AI system increases automation rate and transactions-per-employee, quarter over quarter.

Technology cost per transaction falls while volume grows.

Architecture, systems, and AI stand up to any technical due diligence.

Hiring runs fast and consistently — senior hires close within six weeks, one bar across interviewers.

What you bring

• Senior engineering leadership at scale — run and grown multi-team organizations, ideally across more than one location.
• Deep experience with transactional platforms, ERP-style systems, or comparable systems of record — ideally including regulated financial flows (payments, lending, KYC) and re-platforming/carve-outs in a live environment.
• Hands-on track record shipping AI/ML in production, with real judgment on where agentic AI creates leverage today.
• A demonstrated AI-first, lean mindset — you’ve actually used AI in your own org to raise output without growing headcount, not just talked about it.
• Strong architectural judgment, with security and data protection built into your decisions, and enough technical depth to stay close to the code.
• Judgment on where reliability must be absolute vs. where speed wins — fast on everything reversible, a high correctness bar on irreversible, money-touching paths.
• Plus: enabling non-engineering teams to build safely with AI, with the communication skills to carry that across a company.
• Plus: comfort in a company where operations come first and software follows.
• A commercial mindset — engineering decisions anchored in unit economics, working capital, and speed for buyers and suppliers.
• Plus: experience with Salesforce as part of a larger system landscape.
• Practical details

Location: Berlin. Process: conversations with the CPO, the CEO, Chief of Staff, and members of the engineering team.

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