AI Engineering Team Leader

XTB is a global company from the financial industry, focusing on online trading of financial instruments. We are the largest FinTech in Poland and a leader in Central and Eastern Europe, and the range of our operations covers several countries, including Asia and South America. At XTB, we focus on the development of our employees, giving them opportunities to gain knowledge and skills in various fields, as well as offering a number of training and development programs. If you are looking for challenges and want to gain valuable experience in an international business environment, XTB is the right place for you. We are a certified Great Place to Work company.

As an AI Engineering Team Leader, you will be the technical authority for our LLMOps and Agentic strategy. We are moving beyond prototypes to build resilient, enterprise-grade systems where evaluation-driven development and full-stack observability are core requirements, not afterthoughts. You will ensure XTB’s ecosystem remains at the forefront of innovation - delivering secure, measurable, and highly reliable experiences for over 1 million investors worldwide.

Responsibilities

• Lead and grow a high-performing AI Engineering team, building a transparent, collaborative, high-performance culture focused on delivering end-to-end AI solutions across architecture, backend services, data access layers, evaluation, observability, and production operations, 
  

• Own the technical vision for XTB’s AI ecosystem, keeping it current, pragmatic, and aligned with the company’s evolving product, operational, and regulatory needs. Drive architectural decisions across LLM platforms, RAG systems, agentic workflows, automation layers, and internal AI capabilities used across the organization,
  

• Remain hands-on in day-to-day engineering work by contributing to selected components, supporting complex implementations, conducting code reviews, and helping the team make high-quality technical decisions. Use your technical credibility to unblock delivery, reinforce standards, and strengthen engineering ownership across the team,
  

• Translate abstract ideas, business opportunities, and strategic bets into concrete technical plans, action points, and implementation roadmaps. Break down complex initiatives into clear priorities, engineering milestones, and execution-ready tasks,
  

• Take ownership of the AI technical roadmap, ensuring the team is building not only what is needed now, but also the enabling foundations required for future scale. Continuously manage architectural coherence, platform evolution, and technical debt across all AI solutions,
  

• Design, guide, and scale resilient end-to-end AI systems for commercial use cases, including internal AI products, workflow automation, decision-support services, and agentic solutions embedded into business processes. Ensure those systems are production-ready, observable, secure, and measurable from day one,
  

• Drive engineering standards and best practices for LLMOps, AgentOps, deployment, evaluation-driven development, prompt and tool orchestration, and full-stack observability. Establish sustainable ways of working that improve delivery quality without slowing innovation,
  

• Partner closely with Product, Data, Platform, Security, and other engineering teams to define AI-ready requirements, shape semantic data access patterns, and ensure other teams are equipped to build on top of shared AI capabilities. Act as the go-to technical authority for questions related to AI architecture, AI-ready data, and responsible implementation across Product & Technology,
  

• Support the operational effectiveness of the team by removing blockers, improving workflows, and helping the organization work with AI more productively. Work with the Head of AI to ensure the team operates with clarity, focus, strong execution discipline, and healthy delivery pace,
  

• Educate other teams on how to adopt AI effectively by creating technical standards, defining requirements, reviewing solution approaches, and helping teams distinguish between experimentation and production-grade implementation,
  

• Represent XTB externally by presenting our technical solutions, architectural patterns, and engineering practices at conferences, meetups, and industry events. Help position the company as a credible, modern leader in applied AI for fintech,
  

• Contribute to the company’s broader technology vision by building reliable, high-performance AI systems suitable for the financial market, with strong attention to governance, security, resilience, latency, and operational predictability.

Requirements

• 7+ years of experience in software engineering and AI/ML, including substantial experience delivering commercial AI solutions end-to-end in production environments,
  

• 2+ years of experience in a technical leadership role, such as Tech Lead, Team Leader, or Engineering Manager, with responsibility for both technical direction and team effectiveness,
  

• Proven hands-on experience designing, building, and scaling LLM-based and agentic systems for real business use cases, including orchestration, retrieval, tool usage, workflow automation, and production deployment,
  

• Strong architectural background with the ability to define and evolve technical vision across multiple AI initiatives, balancing speed, maintainability, security, cost, and long-term platform needs,
  

• Practical experience building AI platforms or shared AI capabilities for a broader organization, not only isolated use cases or prototypes,
  

• Deep understanding of Python and strong experience building production-grade APIs, services, and integrations in cloud environments such as Azure, GCP, or AWS,
  

• Hands-on expertise with frameworks and tooling such as LlamaIndex, LangGraph, PydanticAI, FastAPI, vector databases, and modern observability stacks for AI systems,
  

• Practical experience with LLMOps and MLOps practices, including model and prompt versioning, CI/CD, evaluation pipelines, production monitoring, tracing, and feedback loops for continuous improvement,
  

• Strong experience implementing evaluation-driven development, including RAG evaluation, agent evaluation, tool-calling assessment, and measurable quality gates for production AI systems,
  

• Experience embedding AI and automation into business processes, with a clear understanding of when agentic workflows create real value and when simpler solutions are more appropriate,
  

• Ability to turn ambiguous concepts into structured execution plans, define requirements for other teams, and lead cross-functional projects from idea to production,
  

• Strong understanding of technical debt management, platform evolution, and the trade-offs required to maintain delivery speed while preserving system quality,
  

• Experience mentoring engineers, raising engineering standards, and building a culture of ownership, quality, and continuous improvement,
  

• Excellent communication skills in English, with the ability to work effectively across engineering, product, data, and leadership stakeholders, and to represent technical topics clearly both internally and externally.

Nice to have:

• Previous experience in fintech, trading platforms, market data systems, risk-related environments, or other regulated domains with high requirements for reliability and auditability,
  

• Practical experience building Model Context Protocol (MCP) servers and exposing enterprise tools or internal data sources to AI agents,
  

• Strong understanding of AI-ready data architecture, including semantic layers, modern lakehouse approaches, and enterprise data platforms such as Snowflake or Databricks,
  

• Experience with AI-assisted engineering workflows using tools such as Claude Code, Cursor, or GitHub Copilot, combined with a pragmatic approach to rolling those practices out across teams,
  

• Familiarity with latency and cost optimization techniques, including model routing, Small Language Models, caching strategies, and workload segmentation across different AI workloads,
  

• Experience speaking at conferences, publishing technical content, or representing engineering organizations in external communities.

What we offer

• Real influence on the development of the company and the product,
  

• Work in an experienced team that is happy to share its knowledge,
  

• A clear vision of development thanks to regular feedback and clear career paths,
  

• Regular team-building meetings.

Benefits

• A training budget for courses and conferences that interest you,
  

• An extra day off on your birthday,
  

• An extra day off for parents,
  

• Equipment tailored to your needs,
  

• Private medical care and group insurance,
  

• Access to an e-learning platform for learning English and a benefits platform,
  

• Access to a wellbeing platform and the opportunity to take advantage of workshops and private therapy sessions,
  

• Remote work, from the office in Warsaw or from a coworking space in your city.