Senior AI Engineer

3w3 weeks ago

zooplus SE

Madrid, ES · Full-time · €65,000 – €85,000

About this role

With over 25 years’ experience, zooplus SE are trailblazers in the pet e-commerce industry, making over 12 million customers genuinely happier. The Senior AI Engineer embeds AI capabilities across the software development lifecycle to improve engineering productivity, delivery quality, and automation in the PETS area. This role focuses on building intelligent developer workflows using Model Context Protocol, context engineering, LLM orchestration, and AI-driven tooling.

Design and implement AI-driven SDLC accelerators like code generation, review automation, smart testing, and documentation support. Develop MCP-based extensions and context-aware tools to streamline developer workflows. Apply context engineering to enhance LLM reasoning, accuracy, and relevance across engineering use cases.

Headquartered in Munich with offices across Madrid, Krakow, Wroclaw, London, and Vienna, collaborate in an incredible multinational team. Benefit from hybrid working, spending 60% of time in the office and 40% at home, with an option for 20 days abroad in approved countries. Trust each other to get the job done in a diverse and inclusive environment.

Access development opportunities to shape your career path, excellent training and support, and wellbeing offers. Enjoy subsidised transport or bike leasing, plus 20% discount across all products. Discover specific benefits at the local office through blog posts.

Requirements

Tech-agnostic mindset with strong programming experience in Java, Python, JavaScript, or Kotlin
Hands-on experience integrating LLMs into engineering or product workflows
Good understanding of RAG, embeddings, and vector databases
Experience with Docker, Kubernetes, and Terraform
Experience with intelligent test automation and CI/CD pipelines
MCP Engineering – building modular, context-aware AI tooling
Context Engineering – structuring inputs for reliable AI reasoning
AI-Augmented Architecture – integrating AI into engineering systems

Responsibilities

Design and implement AI-driven SDLC accelerators such as code generation, review automation, smart testing, and documentation support
Develop MCP-based extensions and context-aware tools to streamline developer workflows
Apply context engineering to improve LLM reasoning, accuracy, and relevance across engineering use cases
Build LLM-integrated CI/CD workflows for pull request analysis, test automation, and deployment support
Collaborate with engineering, platform, and product teams to identify and scale AI adoption opportunities