AI Engineer

Description

Xebia is a global AI-first, digital transformation, and engineering partner.

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.

Responsibilities

• 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.
• Your profile:
• 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.

Requirements

• 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