A
HybridFull-timeExecutivePlatform Development
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Andercore is the AI-native supplier of industrial materials for energy, infrastructure, and construction in wholesale and beyond.
We trade with global suppliers and distribute to European customers on our own account. Our AI runs the full trade and distribution end-to-end: sourcing, quality, pricing, sales, logistics, and embedded financing. For the customer, it feels like buying from their preferred local supplier; for our partners, it is the most convenient and safe way to do business across borders. Behind it, our software and agentic AI do the heavy lifting that used to take an asset-intensive supply chain with four or five intermediaries and weeks of manual coordination.
Where we are today:
Strong triple-digit-million euro turnover
Seven European markets live
80+ people across Berlin (HQ), offices in London, Mumbai, and Shanghai
$40M Series B just closed, $75M raised to date from Atomico, Project A, and Inven Capital, institutional financing from international banks.
We are building the world's first and last industrial-grade AI operating system for materials, redefining how global trade works in one of the largest and most essential industries on earth.
We are looking for an experienced CTO who has built production-grade AI systems - not just shipped features on top of foundation models. You have deep architectural intuition, you've led engineering organizations through inflection points, and you understand that in a marketplace business, the quality of your decision engines is your competitive moat.
You will inherit a working platform with real transaction volume, a 20-person engineering team, and an AI architecture that needs to evolve from workflow automation into fully autonomous commercial orchestration. Your mandate is to take a system that works and make it defensible, scalable, and increasingly self-improving.
This is the core of the role. You will evolve our existing agent layer from assisted automation into a multi-agent system capable of making binding commercial decisions across pricing, procurement, logistics, and financing — without human handoff.
Concretely, this means:
Designing a modular, event-driven multi-agent framework where agents have well-defined scopes, shared memory, and coordinated execution - not a monolithic "AI layer" pasted onto a backend.
Moving beyond prompt-chained LLM workflows toward tool-augmented, stateful agents that reason over real-time market data, inventory positions, credit exposure, and logistics constraints simultaneously.
Architecting feedback loops: agents that learn from trade outcomes, pricing performance, and fulfillment results to continuously update their decision logic - blending reinforcement signals with structured fine-tuning where appropriate.
Building the observability and evaluation infrastructure that makes agent behavior auditable, debuggable, and improvable.
Ensuring the architecture is model-agnostic - the system must not be structurally dependent on any single foundation model provider.
Embedded working capital is central to our business model. The AI system must not just understand trade flows - it must reason about the capital that moves alongside them.
You will architect:
Algorithmic working capital allocation - real-time credit limit management, dynamic exposure modeling, and automated financing triggers embedded directly into trade execution.
AI-driven credit risk assessment that processes counterparty signals, transaction history, and market conditions continuously, not in batch.
Liquidity and margin optimization in trade decisions is not a downstream financial process.
Compliance and auditability infrastructure that meets the regulatory requirements of financial products operating across multiple jurisdictions.
The goal: capital flows that are programmable, observable, and continuously optimized by the same intelligence layer that executes trade.
You will scale a high-performance engineering organization that ships with discipline and speed.
Develop a senior technical leadership layer, with strong talents who own architectural domains, not just sprint tickets.
Structuring governance frameworks for outsourced engineering - quality gates, integration standards, code review requirements, and security baselines that don't create a two-tier codebase.
Introduce clear ownership models: every system has an owner; every incident has an accountable team; every architectural decision has a record.
Build an engineering culture that treats production reliability, evaluation rigor, and system observability as professional norms, not occasional projects.
Hire selectively and precisely - a small number of high-leverage additions over broad headcount growth.
Communicate the technical roadmap clearly at leadership and board level, including honest trade-off framing and risk visibility.
Hard requirements:
10+ years of engineering experience, with at least 5 in senior technical leadership at scale-ups or high-growth companies.
Demonstrated experience building and operating multi-agent AI systems in production - not prototype or research contexts.
Deep backend and distributed systems expertise: event streaming, microservices, API design, database scaling, observability stacks.
Experience operating AI systems in regulated or high-stakes commercial environments - where model behavior has financial or operational consequences.
Track record of scaling engineering teams from 10 to 30+ people, including developing internal technical leadership.
Familiarity with fintech infrastructure - credit systems, payment flows, or embedded finance is a meaningful advantage.
Experience designing and governing hybrid engineering models with external delivery partners
Technical depth you'll need:
Strong intuitions about LLM selection, fine-tuning, and evaluation — you know when to use a foundation model, when to fine-tune, and when to build something else entirely.
Hands-on familiarity with agentic frameworks (LangGraph, custom orchestration, or equivalent) and their production failure modes.
Architectural fluency in streaming data, real-time inference, and feature engineering for production ML systems.
Understanding of AI system reliability - rate limits, fallback strategies, latency budgets, cost modeling, and evaluation pipelines.
The kind of person who succeeds with us:
You are architecturally strong and care about the quality of systems. You are also commercially strong - you understand that engineering decisions are capital allocation decisions and need to have a business impact. You communicate complex trade-offs clearly. You hire people better than you in their domains, and you build organizations that don't depend on heroics.
At 6 months:
Clear architectural direction established and communicated: AI Agent roadmap, backend refereeing priorities, and hybrid delivery model in place
Engineering ownership model and performance standards implemented
Observability and evaluation infrastructure operational
At 12 months:
Majority of pricing, booking and procurement workflow was orchestrated by AI agents without human intervention
Working capital allocation logic running algorithmically with measurable reduction/decrease in capital exposition and volatility
Platform reliability materially improved
hybrid engineering capacity model operational with no degradation in architectural quality
At 24 months:
Fully autonomous trade execution across core workflow
Ai system demonstrably self-improving - feedback loops generating uplift in pricing margin and operational efficiency which can be measured
Engineering organisation capable of scaling to 40+ people without architectural regression
Technology is recognised as a structural competitive advantage
We are an equal-opportunity employer and welcome applicants from all backgrounds, regardless of race, ethnicity, gender identity or expression, sexual orientation, religion, age, disability, or any other characteristic. We believe that diversity drives innovation, creativity, and collective strength.