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Polska
Remote only
Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.
We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.
In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
Leading AI adoption and enablement programs for client engineering teams.
Running workshops, demos, training sessions, and pair-programming activities that translate AI capabilities into practical day-to-day engineering workflows.
Designing and implementing AI-augmented workflows and automations across the software development lifecycle.
Evaluating and benchmarking AI tools, agents, and coding assistants, turning technical findings into clear recommendations.
Experimenting with emerging AI technologies and identifying practical use cases for software engineering teams.
Contributing to Xebia's Center of Excellence for Engineering Productivity by developing standards, playbooks, cookbooks, and best practices for AI in software development.
Participating in CoE activities and helping shape the direction of Xebia's AI@Engineering initiative.
Advising client engineering leaders on AI adoption strategies, opportunities, and practical implementation.
Combining strategic consulting with hands-on technical work to help engineering organizations adopt AI effectively.
Several years of experience in software engineering, engineering productivity, DevOps, developer experience, or a closely related technical field.
Hands-on experience using AI tools, LLMs, coding assistants, and AI agents to improve software engineering workflows.
Practical understanding of how AI can be applied across the Software Development Lifecycle (SDLC).
Experience designing or implementing AI-enabled workflows, automations, or developer productivity solutions.
Experience working directly with engineering teams and/or clients in a consulting, enablement, or advisory capacity.
Ability to evaluate new AI technologies pragmatically and translate technical capabilities into business and engineering value.
Strong communication and presentation skills, with confidence working with both engineers and engineering leadership.
Experience facilitating technical workshops, demos, training sessions, or enablement programs.
Strong ownership and ability to work independently in a fast-changing environment.
Fluent professional English (C1) for client-facing communication and presentations.
Work from the European Union region and a work permit are required.
Practical experience applying AI across multiple SDLC phases, such as AI-assisted testing, code review, documentation, debugging, requirements analysis, or software design.
Familiarity with LLMOps and AI platform concepts, such as AI gateways, observability, cost management, or model routing.
Experience creating technical content, including guides, playbooks, blog posts, cookbooks, or internal knowledge-base materials.
Familiarity with AI adoption maturity models, engineering productivity metrics, DORA metrics, or structured assessment methodologies.
Awareness of or hands-on experience with Model Context Protocol (MCP), MCP servers, and tool integrations.
CV review – HR call – Interview – Client Interview – Decision
