Senior AI Platform 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 also in Asia and South America.

• 4+ years as Platform Engineer, Backend Engineer, or AI Engineer with platform/service delivery experience.
• Proficient in software engineering fundamentals, including Python, Go, or Java, clean code practices, and system design.
• Production experience with container orchestration (Kubernetes) and ArgoCD for GitOps.
• Expert knowledge of GitLab CI/CD: pipeline design, deployment management, build optimization.
• Experience with LLM frameworks and agentic systems (e.g., LangGraph, Pydantic AI).
• Knowledge of RAG systems: vector databases, similarity search, prompt engineering.
• Understanding of LLM routing and optimization for cost, latency, and multi-model orchestration.
• Experience managing gateway/proxy infrastructure (API gateways, reverse proxies, load balancing).
• Strong foundation in infrastructure-as-code, version control (Git), and CI/CD best practices.
• Excellent communication skills for collaboration with AI/ML teams, data engineers, and product managers.

Nice to have:

• Experience building prompt management and versioning systems.
• Knowledge of semantic caching or prompt optimization techniques.
• Experience with multi-tenant SaaS or PaaS platform design.
• Familiarity with evaluation frameworks for LLM outputs or agentic behaviour.
• Experience with vector database optimization or semantic search.
• Knowledge of advanced deployment patterns (canary, blue-green, A/B testing) for AI services.

• Design and implement an enterprise AI platform with LLM-based routing, model selection, fallback strategies, and cost optimization.
• Build and maintain MCP (Model Context Protocol) gateway infrastructure integrating enterprise tools and AI agents.
• Architect LangGraph-based orchestration frameworks for multi-step AI application workflows.
• Implement RAG platform components: vector search integration, prompt optimization, and chunking strategies.
• Establish observability using tools like Langfuse: tracing, cost monitoring, token usage analytics, LLM quality metrics.
• Design and optimize APIs for LLM interactions.
• Build deployment and orchestration pipelines using Kubernetes (K8s) with ArgoCD for GitOps.
• Establish CI/CD infrastructure using GitLab CI/CD for automated testing, deployment, and versioning.
• Implement security, authentication, and rate limiting for AI endpoints ensuring multi-tenant isolation.
• Create self-service developer interfaces and documentation for non-AI-experts.
• Design prompt management and versioning systems for collaboration and tracking.
• Lead technical initiatives, conduct code reviews, mentor engineers, and foster AI-first engineering culture.