Contribute what works back to our shared tooling so other teams benefit.
Bring business and system context into your work through our shared knowledge base and connections to Jira and Confluence, and add what only lives in people's heads so that colleagues and agents can use it.
Apply judgement on AI-specific risks: prompt injection, personal data and secrets in prompts, licensing, and wrong AI output reaching production.
Try new AI capabilities early and share what you learn (what worked, what didn't, what it cost) with our internal AI-native community.
Use the speed AI gives you to shorten the loop, not to build more unvalidated software - prototype, show it, learn, discard or harden.
Spend AI credits where they create value: know roughly what a task costs and stop when more spend no longer pays off.
Work directly with business stakeholders or your product manager across the whole lifecycle: shape the problem, test feasibility with prototypes, investigate production issues and follow up on adoption.
Communicate trade-offs clearly to technical and non-technical stakeholders across countries and departments
We are looking for these competencies
BSc or MSc in Computer Science or comparable education.
Solid experience building and operating production software, including hands-on delivery with coding agents on real codebases.
Curiosity to keep up with AI capabilities as they change, and the habit of sharing what you learn.
Comfortable working across .NET, Python and TypeScript.
Hands-on experience with cloud services (Azure preferred), containerisation, and CI/CD pipelines.
Solid testing discipline (unit, integration, contract) and an instinct for what AI-generated code needs covered.
A working understanding of how large language models behave and where they fail, and the judgement to know when not to use AI.
Awareness of AI-specific security and compliance risks (prompt injection, data leakage, secrets in context, IP and licensing).
Proven track record of working directly with stakeholders and end users, not only through a product owner.
Bias for short feedback loops: shipping small, validating with real users, and changing course on evidence.
Passion for finding simple solutions to complex problems.
Proficient in both verbal and written English.
What is beneficial
Experience with RAG pipelines, embeddings, or vector search (e.g. pgvector)• Prompt evaluation, LLM-as-judge, or AI output quality measurement experience
Experience introducing AI-assisted ways of working to a team (enablement, templates, guidelines)
Familiarity with DORA or SPACE metrics and engineering-productivity measurement
Experience with product discovery, user research, or value-case definition
Domain exposure to heavy industry, manufacturing, logistics, or supply chain
Experience in workflow automation or orchestration
Experience working in multi-country or multi-cultural organisations
Active GitHub profile with open-source contributions
What we offer
Employment contract in well-established company that is part of Heidelberg Materials Group;
Comprehensive benefit package, including private medical care, subsidized multisport card, holiday and childrens‘s summer camp allowances, culture and sport subsidies, fresh fruit at work;
Extensive range of internal and external training opportunities;
Cultural events and sports competitions for employees;
Annual Bonus.
What’s going to happen next?
Once you apply to this job advert, our recruiter will reach out to you within a week. If there is interest on both sides, we will schedule a pre-screening call to learn more about your experience and motivation to join our team.
