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. | LNDS on LinkedIn
We are hiring an AI Engineer to help strengthen LNDS’s AI capabilities and enhance our services through the practical use of AI. The role focuses on building AI-powered tools, agents, and workflows that improve how services are delivered to partners, making them faster, more efficient, and more scalable. The position also contributes to reducing operational overhead and enabling teams to focus on higher-value activities, while supporting long-term AI readiness across the organisation.
As an AI Engineer, you will be part of the DataOps team and work closely with the service delivery team to design and implement 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 disciplined engineering, careful experimentation, and clear documentation. The role focuses on applied AI engineering: turning real operational and service needs into practical, reliable solutions rather than developing models from scratch.
- Contribute to improving LNDS services by integrating AI into service delivery workflows
- Build and support AI-powered tools, agents, assistants and internal applications to support internal teams and partner-facing activities
- 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
- Design and implement basic evaluation and benchmarking approaches for AI systems (e.g.,prompt evaluation, retrieval quality, 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 technical and non-technical colleagues to translate internal needs into practical solutions. Contribute to the responsible, sustainable adoption of AI across the organisation
- Contribute to EU projects as required.
Required:
- BSc or MSc in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, Physics and related fields
- 2–5 years of relevant experience in AI, machine learning, software engineering, data science, and a related technical area
- Good grounding in Python and sound software engineering practices
- Hands-on experience with Unix/Linux environments as a primary platform
- Hands-on experience with LLMs-based systems (RAGs, assistants, workflow automation, agentic systems or model evaluation)
- Practical familiarity with open-source tools, libraries and development workflows
- Ability to work independently on well-scoped tasks and solve problems in a structured way• High standards of accuracy, reliability and technical discipline
- Sensitivity to internal user needs and ability to iterate based on feedback
- Experience of working in Agile-minded teams
- Strong written and verbal communication skills and willingness to learn.
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