Senior 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 emphasizes employee growth.

• 5+ years of experience in architecting and deploying production-grade AI/ML solutions in Azure, GCP, or AWS ecosystems.
• Mastery of Python and solid foundation in the full ML lifecycle including classical statistical models, deep learning, and NLP.
• Deep hands-on expertise with LLM orchestration and data frameworks like LlamaIndex, LangGraph, or PydanticAI.
• Experience building complex, multi-step agentic workflows.
• Practical experience developing and deploying Model Context Protocol (MCP) servers.
• Strong background in MLOps and LLMOps pipelines including model versioning, automated deployment, and full-stack observability.
• Experience implementing evaluation-driven development using frameworks like RAG-eval and agentic evaluations.
• Proficiency in building high-performance APIs with FastAPI.
• Experience designing semantic layers on enterprise data platforms such as Snowflake or Databricks integrated with vector databases (Weaviate, pgvector).
• Ability to perform Buy vs Build analyses and translate business requirements into technical architectures balancing innovation with ROI and FinOps.
• Experience leading technical initiatives, conducting code reviews, and mentoring engineers.
• Excellent communication skills and ability to work cross-functionally in English.

• Design and scale resilient, end-to-end architectures for LLM and agentic solutions within a high-load, low-latency fintech environment.
• Act as a strategic technical partner to the Head of AI, leading architectural reviews and Buy vs Build analyses for greenfield AI projects.
• Take full ownership of the AI product lifecycle, establishing rigorous standards for LLMOps, FinOps, and automated deployment pipelines from PoC to production.
• Spearhead the adoption of Agentic Evals and RAG-eval frameworks to turn fine-grained observability signals into actionable system improvements.
• Collaborate with Data and Product teams to architect AI-ready data layers and MCP servers, ensuring seamless agentic access to semantic layers in Snowflake and Databricks.
• Foster a culture of technical excellence by mentoring engineers and defining best practices for AI-assisted development (Claude Code, Cursor) to elevate team productivity.
• Bridge technical complexity with business ROI, ensuring all solutions adhere to financial AI Governance and security standards.