# LNDS - 096 - AI Engineer II - Subsidy Projects (fixed term 24 months) at Lnds

> Luxembourg · Mid-level · ML / AI · open. Source: https://alderhq.app/jobs/personio%3Alnds%3A2698055 (read 2026-11-09).

## Facts (observed)

- Company: [Lnds](https://alderhq.app/companies/lnds)
- Location text: Luxembourg - Esch-sur-Alzette
- Where it can be done from (inferred): Luxembourg (evidence: "Location: Luxembourg - Esch-sur-Alzette")
- Posted: 2026-07-03 (128 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://lnds.jobs.personio.de/job/2698055
- Skills listed (inferred): Python, Docker, LLMs, Distributed systems

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

### Who we are

www.lnds.lu \| LNDS on LinkedIn

### Purpose of the job / project

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.

### What you will do

- 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.
### Who you are

- 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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/personio%3Alnds%3A2698055 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.