AI Engineer

• hands-on experience building AI or agent-based solutions,
• experience designing or implementing LLM/AI agent workflows,
• experience with model integration and routing, including consideration of cost, latency and risk,
• experience designing controlled tool interfaces for AI agents,
• knowledge of MCP-based tool interfaces or comparable permissioned agent-tool integration approaches,
• practical experience with Document AI capabilities such as OCR, structured extraction, PII detection or masking,
• experience with retrieval architectures using embeddings and vector or lexical search,
• experience designing evaluation approaches for AI systems, including model or prompt evaluation,
• understanding of observability requirements for agent behaviour,
• ability to design AI solutions that operate through permissioned, logged and auditable interfaces,
• ability to collaborate across prototype and production-oriented engineering streams,
• ready to join within 2 weeks.

Work from the European Union region and a work permit are required.

• experience with self-hosted models in environments with data residency constraints,
• experience with knowledge graphs as contextual sources for AI agents,
• experience with multi-agent or multi-role decision patterns,
• experience with AI applications supporting credit, KYC, compliance, document processing or exception management,
• familiarity with deterministic core banking systems,
• experience with A/B testing of models or prompts,
• experience working in financial services, leasing or vendor finance.

Recruitment Process

CV review – HR call – Interview – Client Interview – Decision

• building agents supporting credit, KYC/compliance, document handling and exception triage,
• implementing multi-role decision patterns for high-impact cases, including analyst, critic and guardian roles,
• developing a model gateway capable of routing requests between models according to cost, risk and latency,
• supporting self-hosted models where data residency requirements make this necessary,
• building MCP tool interfaces that restrict agent actions to permissioned and logged tools,
• developing Document AI capabilities covering OCR, information extraction, PII detection and masking,
• building retrieval and context capabilities using embeddings, vector search, lexical search and a knowledge graph,
• developing an evaluation framework for AI behaviour, including evals, A/B testing of models and prompts, and observability,
• working with the Foundation and Lab streams to ensure AI concepts can be implemented on the target platform.