AI Engineer I

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.

• 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.

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