LNDS - 097 - AI Engineer I - Subsidy Projects (fixed term 24 months)
Who we are Luxembourg National Data Service (LNDS) is a brand of PNED G.I.E. an organisation created by the Luxembourg Government, to implement Luxembourg’s strategies in research, innovation, and digitalisation. LNDS enables value creation from secondary use of data, for public and private partners and supports the sharing and re-use of public sector data, in a trustable manner. The LNDS service portfolio includes know-how, capabilities, tools, infrastructure, and data services. Through efficient & responsible use of data and improving the secondary use of data, LNDS will support the acceleration of economic, ecological, and societal transitions. www.lnds.lu | LNDS on LinkedIn
Purpose of the job / project As an AI Engineer, you will be part of the DataOps team and collaborate with service delivery teams to contribute to the design, implementation, and continuous improvement of AI-powered solutions that enhance LNDS services offered to partners.
This includes:
• integrating LLMs and other AI technologies into service workflows • building internal tools and agents • improving how teams deliver, combine, and scale services
You will work in a GNU Linux/Unix-first, FOSS-oriented environment and contribute through hands-on experimentation, continuous learning, disciplined engineering practices, and clear documentation.
What you will do • Contribute to improving LNDS services by integrating AI into service delivery workflows • Contribute to building and supporting AI-powered tools, agents, assistants, and internal applications alongside colleagues across the organisation. • Help simplify and accelerate service delivery by automating repetitive or manual tasks • Assist with evaluating, testing, and integrating AI/ML models and related tooling for internal use cases • Contribute to the implementation of evaluation and benchmarking approaches for AI systems, including prompt evaluation, retrieval quality assessment, and monitoring of system behaviour. • Contribute to AI workflow design, including data preparation, prompting strategies, evaluation and deployment patterns • Help improve the reliability, reproducibility, and maintainability of AI development practices • Contribute to ensure that AI systems are deployed responsibly, with appropriate validation (including human in the loop where needed) and awareness of privacy, risk, accuracy, and context • Contribute to AI-driven initiatives such as domain-specific applications (e.g. knowledge systems, legal or regulatory use cases, internal assistants) • Work productively with FOSS-based tools and frameworks in a Unix/Linux environment • Document technical decisions, implementation details, and operational procedures clearly and accurately • Collaborate with colleagues from different disciplines to understand needs, share ideas, and contribute to practical AI-enabled solutions. • Contribute to the responsible, sustainable adoption of AI across the organisation • Contribute to EU projects as required.
Who you are Required
• BSc or MSc in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, Physics and related field • 0–2 years of relevant experience gained through employment, internships, research projects, academic work, open-source contributions, or personal projects in AI, machine learning, software engineering, data science, or related technical fields. • Familiarity with Unix/Linux environments through coursework, projects, internships, or professional experience. • Exposure to LLM-based systems such as RAG applications, AI assistants, workflow automation, prompt engineering, or model evaluation through practical projects or professional experience. • Practical familiarity with open-source tools, libraries and development workflows • Takes ownership of assigned work, seeks feedback proactively, and approaches problems in a logical and structured way. • Demonstrates attention to detail, curiosity, and a commitment to delivering reliable and maintainable solutions. • Sensitivity to internal user needs and ability to iterate based on feedback • Experience working collaboratively in team-based environments using agile, iterative, or project-based ways of working. • Strong written and verbal communication skills, a collaborative mindset, and enthusiasm for continuous learning and skill development.
Nice to have (considered as advantages)
• Awareness of data privacy, security, and responsible AI considerations in system design • Familiarity with containers, REST, CI/CD or MLOps-related tooling • Exposure to vector or graph databases, experiment tracking, model serving, or observability tooling • Contributions to open-source projects or demonstrable personal technical projects