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# Chief Technology Officer

- **Company:** Andercore
- **Location:** Berlin, Germany
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Databases, Distributed Systems, Payment Systems, Data Streaming, Feature Engineering, Evaluation Pipelines, System Availability, Large Language Models, Multi-Agent Systems, Reliability of Systems, Backend, Agentic-AI, Real-time Inference, Machine Learning Operations, Api Design, Microservices
- **Published:** November 13, 2026
- **Apply:** https://startup.jobs/chief-technology-officer-andercore-8297360

## About the Role

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., 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, * 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.

## Description

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., * 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