Chief Technology Officer

As a key growth accelerator, A11 partners with Europe’s most ambitious startups and scaleups, corporates, and SMBs to connect them with top-tier talent ready to make an impact.

We’re looking for an experienced Chief Technology Officer (m/f/d) to join one of our Ecosystem Companies:
Andercore.

As Chief Technology Officer, you will own the evolution of the company’s agentic AI architecture, AI-native fintech integration, and engineering leadership.

Join Andercore – A next-generation supply chain platform, the company is redefining how one of the world’s largest industries sources, distributes, and finances materials. Engineered to unlock the full potential of global trade, it harnesses agentic AI and workflow automation to provide seamless, high-efficiency access to premium materials ce its commercial rollout, the platform has fueled exponential growth, optimizing procurement for thousands of businesses while setting new benchmarks for speed, transparency, and efficiency.

Its competitive edge lies in full digitization and AI-driven supply chain management automation, eliminating inefficiencies and creating a direct, intelligent bridge between suppliers and buyers. With a team of 80+ FTEs across Europe (Berlin HQ), China, and India, the company is rapidly scaling toward profitability, driving triple-digit million GMV, and setting new industry standards in autonomous, AI-powered supply chain orchestration.

Agentic AI Architecture

• Design 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.
• Move 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.
• Architect 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.
• Build the observability and evaluation infrastructure that makes agent behavior auditable, debuggable, and improvable.
• Ensure the architecture is model-agnostic - the system must not be structurally dependent on any single foundation model provider.
• Architect algorithmic working capital allocation - real-time credit limit management, dynamic exposure modeling, and automated financing triggers embedded directly into trade execution.
• Design AI-driven credit risk assessment that processes counter party signals, transaction history, and market conditions continuously, not in batch.
• Integrate liquidity and margin optimization in trade decisions, not as a downstream financial process.
• Build compliance and auditability infrastructure that meets the regulatory requirements of financial products operating across multiple jurisdictions.

Engineering Leadership

• 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.
• Structure 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…