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.
🏢 About Xebia
The project combines DevOps, platform engineering, security, observability, and AI/MLOps within a distributed international environment. You will work closely with AI Engineers and software development teams using Claude and other LLM providers, helping establish the operational capabilities required to move AI agent solutions safely from development into production.
🎯 The Role
You will work closely with AI Engineers and software development teams using Claude and other LLM providers, helping establish the operational capabilities required to move AI agent solutions safely from development into production.
✅ Key Responsibilities
Design, implement, and maintain deployment processes for AI agents built on the .NET ecosystem
Support multiple development teams working in parallel across separate AI agent repositories
Establish and maintain GitHub Enterprise best practices, including branching strategies, Pull Request workflows, code reviews, and repository governance
Implement and maintain CI/CD pipelines supporting reliable and repeatable AI agent delivery
Manage secrets, credentials, tokens, and environment configurations following enterprise security best practices
Implement secure access management based on the principle of least privilege
Establish logging, monitoring, auditing, and operational visibility for AI agents and their integrations
Implement observability solutions including dashboards, alerting, and distributed tracing where appropriate
Collaborate closely with AI Engineers working with Claude and other LLM providers
Support the future integration of Azure-hosted and self-hosted language models
Contribute to secure and compliant development and deployment processes, including GDPR-related requirements
Ensure test and development environments follow appropriate security and data handling practices
Participate in technical reviews, architecture discussions, and operational readiness assessments
Contribute to infrastructure automation, configuration management, and continuous improvement of the AI platform
Support engineering teams in adopting reliable and scalable DevOps and MLOps practices
📌 Required Qualifications
5+ years of commercial experience in DevOps, Platform Engineering, SRE, Infrastructure Engineering, or similar roles
Strong hands-on experience with GitHub Enterprise and Pull Request-based development workflows
Proven experience designing and maintaining CI/CD pipelines
Strong understanding of environment management, release processes, and deployment automation
Practical experience with infrastructure automation and configuration management
Strong Linux administration and cloud engineering background
Experience working with enterprise-scale software development environments
Strong understanding of secrets management, identity and access management, and least-privilege security models
Experience implementing audit logging and secure software delivery practices
Understanding of enterprise security, governance, and compliance requirements
Hands-on experience with logging, monitoring, distributed tracing, operational dashboards, and incident troubleshooting
Previous experience supporting AI, GenAI, LLM, or MLOps workloads
Familiarity with Anthropic Claude, OpenAI, Azure OpenAI, or similar LLM platforms
Strong communication and collaboration skills
Ability to work independently and take ownership in distributed international teams
Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery
Work from the European Union region and a work permit are required
⭐ Desirable Experience
Hands-on experience with Microsoft Azure
Experience working with Azure OpenAI
Experience with Docker and other containerized deployment technologies
Experience supporting .NET development teams
Experience working on AI Agent, GenAI, RAG, or Agentic AI initiatives
Familiarity with MLOps platforms, workflows, and model lifecycle management
Experience working within enterprise governance, compliance, or regulated environments
Experience implementing security and operational controls specifically for AI workloads
Practical experience using AI-assisted development tools such as Claude Code, GitHub Copilot, Cursor, or similar technologies
Experience designing scalable platform capabilities that support multiple AI engineering teams
Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work
Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches
