(Junior) AI Native Software Engineer (all genders)
Remote • Kronberg
Posted 24d ago
Job Location
Kronberg
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Engineer
About the job
We are seeking AI-native engineers who integrate AI as a core part of their workflow, not as an add-on. In this role, you will design, build, and ship production-grade software across the full stack, leveraging AI-assisted tooling as a standard daily practice alongside your core engineering skills. You will work on client programs across various industries, building software that connects to and supports agentic AI systems. This involves understanding how your full-stack work integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This is a core engineering function with a direct pathway to advanced roles for those who develop agentic depth.
Responsibilities
- Utilize AI coding assistants daily to enhance productivity and output quality.
- Integrate LLM APIs into production applications, managing token limits and latency.
- Apply AI across the software delivery lifecycle, including AI-generated tests, debugging, code review, and prompt engineering.
- Own the quality and reliability of AI-generated outputs, exercising engineering judgment on their production readiness.
- Define and track KPIs for AI-assisted workflows and present metrics to stakeholders.
- Manage end-to-end delivery from design to production support within Agile sprint cycles.
- Contribute to shared knowledge bases, reusable components, and internal AI tooling standards.
- Build and integrate application layers, APIs, and interfaces connecting full-stack systems to agentic backends.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field.
- Production software engineering experience or equivalent demonstrated through projects.
- Proficiency in at least one backend language: Python, Java, or TypeScript.
- Demonstrated hands-on experience using AI tools in daily engineering work, with practical examples of problem-solving and iteration.
- Direct experience calling LLM APIs in production code, understanding token management, latency, and cost tradeoffs.
- Basic understanding of web technologies (JavaScript, HTML, CSS).
- Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines.
- Understanding of Agile delivery fundamentals.
- Experience with SQL or NoSQL databases.
- Ability to validate, evaluate, and improve AI-generated outputs, understanding AI limitations and responsible use.
- Familiarity with agentic system concepts, including orchestration frameworks (LangChain, LangGraph, or equivalent) and RAG pipelines.
- Very good German and English skills.