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Senior Cloud Software Engineer, Backend – AI
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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and developing cloud-native applications using Java/Kotlin and Spring Boot, with a strong focus on integrating AI components and optimizing backend systems. Proficient in containerization, orchestration, and CI/CD practices to deliver production-ready enterprise solutions.
Highest-signal resume keywords
Java/Kotlin Backend DevelopmentSpring BootDocker and KubernetesCI/CD Pipeline OptimizationAI Integration and MLOps
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Design and develop cloud-native applications with Java/Kotlin and Spring Boot
- Design and implement REST APIs for enterprise systems
- Containerize and orchestrate applications with Docker and Kubernetes/OpenShift
- Build and optimize CI/CD pipelines
- Migrate and modernize existing backend systems
- Develop RAG systems with LangChain/LangChain4j
- Implement MCP servers to integrate existing APIs with LLMs
- Build agent-based architectures and enable agent-to-agent communication
- Integrate vector databases and embedding technologies
- Deploy and monitor AI components in the cloud (MLOps)
- Provide technical consulting to enterprise customers on AI integration projects
- Make architectural decisions at the intersection of backend development and AI
- Evolve internal standards and best practices
- Develop production-ready enterprise solutions in a 50/50 split between backend development and AI integration
Requirements
What you’ll need- 5+ years of experience in backend development with Java/Kotlin
- Strong knowledge of Spring Boot or Jakarta EE
- Hands-on experience with Kubernetes and at least one cloud platform (AWS/Azure/GCP)
- Hands-on experience with Docker and CI/CD tools (GitLab, Jenkins, GitHub Actions)
- Practical experience with LLMs—whether through a personal project, proof of concept, or production deployment
- Understanding of RAG concepts or the motivation to learn them quickly
- Business-fluent German
- Fluent English
- Python skills for ML/AI components (nice to have)
- Experience with LangChain, LlamaIndex, or LangChain4j (nice to have)
- Knowledge of vector databases (Pinecone, Weaviate, Qdrant, ChromaDB) (nice to have)
- Experience with the Model Context Protocol (MCP) or agent frameworks (nice to have)
- Experience with prompt engineering or model fine-tuning (nice to have)
- GitOps with Argo CD or Flux (nice to have)
- Strong communication skills in direct customer interactions
- An architectural mindset
- Eagerness to learn and a pragmatic working style
Benefits
Comp & perks- Permanent employment contract
- Paid certifications—AWS, Azure, GCP, and Kubernetes: we cover the costs
- Generous professional development budget for AI courses and conferences
- Access to leading AI communities and meetups (speaking opportunities welcome)
- Challenging enterprise projects with renowned clients
- Freedom to choose your tools and hardware—a laptop and phone to suit your preferences
- Innovation lab for proofs of concept
- Modern office with excellent transport links
- Agile teams with short decision-making paths
- Freedom to drive your own technical initiatives
- Regular internal tech talks on AI trends
- Time during working hours to write blogs and technical articles
- Close collaboration within the team—we learn from one another
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
JavaKotlinSpring BootREST API DevelopmentDockerKubernetesCI/CD ToolsLangChainVector DatabasesMLOps
Soft Skills
Strong Communication SkillsArchitectural MindsetEagerness to Learn
Tools & Technologies
AWSAzureGCPGitLabJenkinsGitHub ActionsArgo CDFlux
Industry Keywords
Cloud-Native ApplicationsAI IntegrationRAG SystemsModel Context ProtocolAgent Frameworks