AI Integration Engineer

• Join EGYM as our AI Integration Engineer (m/f/d) in Munich or Berlin! In this pivotal role, you build the practical backbone of AI adoption at EGYM, turning high-impact use cases into secure, reliable, and production-grade systems
• Prototyping and Production: You design and build AI-powered proof of concepts for prioritized internal use cases, then take the ones that prove their value through to production
• AI Implementations: You work with internal MCP servers and implement LLM-based workflows including RAG systems and intelligent automations
• System Integration: You integrate AI solutions into existing systems including Google Workspace, Slack, ERP, CRM, and internal platforms, handling the auth flows and access scopes each one requires
• Technical Architecture: You translate use cases into technical architectures, including how they will be hosted, secured, and run over time
• Cloud Deployment and Management: You design and deploy on Google Cloud, making sensible use of services such as Cloud Run, Secret Manager, and Identity and Access Management, and reasoning about cost
• Compliance and Security: You work with IT and Security to meet review and compliance requirements, respecting each system’s access controls and data ownership
• CI/CD and Tool Stability: You move successful prototypes toward stable internal tools with proper deployment and hosting, version control, and CI/CD
• Production Hardening: You support teams in taking vibe-coded prototypes and turning them into production-ready applications, hardening them with proper architecture, security, testing, and deployment so they can run reliably at scale
• Your training goal for your first 6 months:
• Advance AI-native EGYM: Support EGYM’s shift to an AI-native company from the technical side, embedding solid AI systems and practices into how teams work
• Set the Reliability Bar: Support the stability and quality of internal tools, including those others have built, establishing the security and reliability standard that adoption can depend on
• Ship to Production: Take prioritized use cases from proof of concept to live, production-grade systems that teams can rely on every day

Benefits

• Development budget
• Internal trainings
• EN & DE language courses
• 30 days of holiday
• Flexible working hours
• Hybrid work model
• Wellpass membership
• Inhouse gym
• Lease a bike
• Company & team events
• Sabbatical
• Corporate Benefits

If you’re a strong technical builder who wants to work at the cutting edge of applied AI and Agentic AI workflow automations, this is your opportunity to make a significant impact on our path to becoming an AI-native companyAI and LLM Expertise: You have practical experience building LLM-based systems such as MCP Servers, RAG pipelines, agentic workflows, ideally with at least one running in a real environment

• AI and LLM Expertise: You have practical experience building LLM-based systems such as MCP Servers, RAG pipelines, agentic workflows, ideally with at least one running in a real environment
• Technical Skills: You are fluent in Python, Java or comparable languages and the common AI tooling ecosystem, with the ability to integrate across enterprise APIs
• Experience: You gained solid hands-on software engineering experience (3 to 5 years), comfortable owning code from prototype through to production
• Cloud and Security Knowledge: You have a working knowledge of a major cloud platform, Google Cloud preferred, plus a strong grasp of authentication, authorization, and data security
• Pragmatic Approach: You possess a pragmatic, product-minded approach focused on real user impact
• Communicator and Collaborator: You have strong communication in English, able to explain technical trade-offs to non-technical stakeholders
• Frameworks and Lowcode: You acquired experience with multi-agent frameworks such as CrewAI and low-code platforms such as Dataleap or n8nSystem reliability: You are familiar with CI/CD, observability, and reliability practices for AI or data systems
• System reliability: You are familiar with CI/CD, observability, and reliability practices for AI or data systems