AI Engineer

XTB is a global company from the financial industry, focusing on online trading of financial instruments. It is the largest FinTech in Poland and a leader in Central and Eastern Europe, operating in several countries including Asia and South America. The company offers training and development programs and is a certified Great Place to Work.

• 2-5 years of commercial experience in AI/ML or Software Engineering with a proven track record of deploying systems to production.
• Proficiency in Python and solid understanding of the full ML lifecycle from data preparation to model deployment and monitoring.
• Hands-on expertise in developing Model Context Protocol (MCP) servers or custom tool-calling interfaces.
• Practical experience with LLM orchestration and data frameworks such as LlamaIndex, LangGraph, or LangChain.
• Experience building and maintaining RESTful APIs using FastAPI in cloud-native environments (Azure, GCP, or AWS).
• Solid understanding of database technologies including vector databases like Weaviate or pgvector.
• Familiarity with MLOps/LLMOps concepts including CI/CD pipelines, containerization (Docker), and basic observability.
• Analytical mindset with ability to debug complex issues in non-deterministic AI systems.
• Strong communication skills and ability to collaborate effectively in an Agile, cross-functional environment in English.

Nice to have:

• Hands-on experience with evaluation frameworks (e.g., Langfuse, LangSmith) and observability tools.
• Knowledge of financial systems, trading, or experience in a regulated fintech environment.
• Solid understanding of Data Engineering fundamentals including Spark, Kafka, and modern Lakehouse architectures on platforms like Snowflake or Databricks.
• Familiarity with Small Language Models (SLM) and techniques for strategic cost and latency optimization.
• Proficiency in AI-assisted development tools like Claude Code, Cursor, or GitHub Copilot.

• Implement and maintain end-to-end AI solutions, focusing on the robustness and scalability of LLM pipelines and agentic workflows.
• Develop and optimize RAG systems and AI agents using modern frameworks, ensuring high-quality retrieval and response accuracy.
• Build and integrate production-grade APIs (FastAPI) to expose AI capabilities to the global trading platform and internal business units.
• Participate in the implementation of LLMOps and MLOps standards, including automated testing, model versioning, and full-stack observability.
• Apply evaluation-driven development practices by using frameworks like RAG-eval to systematically measure and improve system performance.
• Collaborate with Product and Data teams to build and maintain MCP servers and data-serving layers that empower AI agents with real-time enterprise data.
• Maintain high code quality through rigorous testing, documentation, and active participation in code reviews.
• Leverage agentic coding tools (Claude Code, Cursor) in daily workflow to maximize engineering efficiency and share learnings with the team.