Senior Software AI Engineer - ADLC (All Genders) at zooplus SE

Madrid, Madrid Community, Spain -

Full Time

Start Date

Immediate

Expiry Date

26 Aug, 26

Salary

0.0

Posted On

28 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Model Context Protocol (MCP), Context Engineering, LLM Orchestration, RAG, Vector Databases, Java, Python, JavaScript, Kotlin, Docker, Kubernetes, Terraform, CI/CD Pipelines, AI-Driven Tooling, Test Automation, GenAI Use Case Identification

Industry

Retail

Description

Company Description With over 25 years’ experience, plus the same enormous passion and ambition we had on day-one, we’re trailblazers in the pet e-commerce industry. Our solutions make over 12 million customers genuinely happier – and that’s something we really care about. We work together to continually adapt; embracing change and the challenges it brings so that we can keep doing better. Join us and discover a place where your potential meets your passion for pets. That’s the power of plus. Job Description The Senior AI Engineer is responsible for embedding AI capabilities across the software development lifecycle (SDLC) to improve engineering productivity, delivery quality, and automation in PETS area of zooplus. The role focuses on building intelligent developer workflows using Model Context Protocol (MCP), context engineering, LLM orchestration, and AI-driven tooling. Key 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. Qualifications Required Qualifications Tech-agnostic mindset with strong programming experience in one or more of the following languages: Java, Python, JavaScript, or Kotlin, or broad working knowledge across multiple of these languages. 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. Core Competencies MCP Engineering – building modular, context-aware AI tooling Context Engineering – structuring inputs for reliable AI reasoning AI-Augmented Architecture – integrating AI into engineering systems Product Thinking – identifying high-value GenAI use cases Additional Information Headquartered in Munich, with offices across Madrid, Krakow, Wroclaw, London and Vienna, we collaborate because we genuinely love working together - so you’ll be at the heart of an incredible, multinational team. We trust each other to get the job done, which means you’ll benefit from hybrid working, spending 60% of your time in the office and 40% at home. There’s an option to work for 20 days abroad in approved countries too. Add to that development opportunities that help you to shape your own career path, excellent training and support, wellbeing offers, subsidised transport or bike leasing, plus 20% discount across all our products, and you’ll begin to get a sense of what sets us apart. Find out more about the specific benefits at your local office in our blog posts. At zooplus, we believe in equal opportunity and support everyone to fulfil their potential. We recognise the power of listening and learning from each other and embrace diversity and inclusion of all. #LI-Hybrid Department: Product & Tech

Responsibilities

The role focuses on embedding AI capabilities across the software development lifecycle to improve engineering productivity and delivery quality. Key tasks include designing AI-driven SDLC accelerators and building MCP-based extensions for intelligent developer workflows.