Senior AI Platform Engineer
New
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XTBFinancial Technology
Warszawa, Prosta 67, Kraków, Gdańsk, Wrocław, Katowice, PolandFull-TimeSenior
Salary20,400 - 25,900 PLN per month
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Job Details
- Experience
- 4+ years as a Platform Engineer, Backend Engineer, or AI Engineer
- Required Skills
- PythonJavaKubernetesGoCI/CDGitLab
Requirements
- 4+ years as a Platform Engineer, Backend Engineer, or AI Engineer with hands-on platform/service delivery experience.
- Proficiency in Python and/or Go/Java.
- Strong software engineering fundamentals including clean code practices and system design.
- Production experience with container orchestration (Kubernetes) and ArgoCD for GitOps deployments.
- Expert-level knowledge of GitLab CI/CD for pipeline design and deployment optimization.
- Practical experience with LLM frameworks and agentic systems (e.g., LangGraph, Pydantic AI).
- Working knowledge of RAG systems including vector databases and similarity search.
- Understanding of LLM routing and optimization including cost-aware model selection and latency reduction.
- Experience building or managing gateway/proxy infrastructure (API gateways, reverse proxies, load balancing).
- Strong foundation in infrastructure-as-code and version control (Git).
Responsibilities
- Design and implement an enterprise AI platform with LLM-based routing, intelligent model selection, fallback strategies, and cost optimization.
- Build and maintain MCP (Model Context Protocol) gateway infrastructure enabling seamless integration of enterprise tools and internal data with AI agents.
- Architect LangGraph-based orchestration frameworks for complex agentic workflows, enabling developers to compose multi-step AI applications.
- Implement RAG (Retrieval-Augmented Generation) platform components: vector search integration, prompt optimization, and chunking strategies.
- Establish comprehensive observability using Langfuse and similar tools: tracing, cost monitoring, token usage analytics, and LLM quality metrics.
- Build deployment and orchestration pipelines using Kubernetes (K8s) with ArgoCD for GitOps-driven AI service deployments.
- Establish CI/CD infrastructure using GitLab CI/CD for automated testing, deployment, and versioning of AI platform components.
- Implement security, authentication, and rate limiting for AI endpoints protecting against abuse and ensuring multi-tenant isolation.
- Create self-service developer interfaces and documentation enabling non-AI-experts to build sophisticated agentic applications.
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