AI engineering intern

Team: AI Enablement
Duration: Approximately three months
Location and working arrangement: Brussels HQ
Working language: English

About the internship

Mobilexpense is looking for an AI Engineering Intern to join the AI Enablement team and contribute to a substantial AI project with real internal users and business value.

During the internship, you will take ownership of one primary project, from technical exploration and architecture through development, testing, deployment, and demonstration. You will work closely with our AI Engineer and collaborate with teams such as Security, Data, DevOps, IT, Product, and other business functions depending on the assignment.

This internship is intended for someone with solid software development foundations and genuine curiosity about artificial intelligence. We do not expect you to already be an experienced AI Engineer, but we are looking for someone who actively experiments with AI, understands that building a reliable AI application involves more than writing prompts, and wants to develop practical expertise in the field.

The final project will be selected according to Mobilexpense priorities, the internship period, and your background and interests. It will be scoped to provide meaningful technical ownership throughout the three-month internship.

Possible internship projects

Internal Knowledge Assistant using RAG

Design and build an assistant that allows employees to retrieve reliable information from internal company knowledge.

This would be an end-to-end Retrieval-Augmented Generation project rather than a simple chatbot. The work could include:

• Document collection, parsing, cleaning, and metadata extraction.

• Chunking strategies for structured and unstructured documents.

• Generating and managing embeddings.

• Storing content in a vector database.

• Implementing semantic vector search, hybrid search, or reranking.

• Building a complete RAG pipeline using an OpenAI model.

• Producing grounded answers with source citations.

• Applying permissions and access boundaries to retrieved content.

• Creating an evaluation dataset to measure retrieval quality, answer accuracy, and hallucinations.

• Exposing the solution through an API or a small internal application.

• Documenting the architecture, limitations, and possible production path.

The objective would be to deliver a technically credible prototype and demonstrate, through evaluation, whether it provides sufficiently reliable answers for a defined internal use case.

Privacy Aware AI Gateway and MCP Service

Design, develop, and deploy an internal service that controls how sensitive information is handled before content is sent to an AI model.

The project could include:

• Building an API or Model Context Protocol server that sits between an internal application and OpenAI.

• Detecting personal, confidential, or customer-related information.

• Applying configurable anonymisation, replacement, or filtering rules.

• Preserving the usefulness and context of the original request after anonymisation.

• Integrating authentication and user or application permissions.

• Implementing structured outputs, error handling, audit logs, and traceability.

• Creating representative test datasets and measuring anonymisation quality.

• Testing potential prompt injection and data leakage scenarios.

• Packaging the solution as a deployable service.

• Collaborating with Security to validate the data-handling approach.

• Collaborating with DevOps on deployment, configuration, secrets management, monitoring, and CI/CD.

• Documenting known limitations and operational requirements.

The objective would be to move beyond an isolated proof of concept and deliver a service that can be deployed and tested in a realistic internal environment.

What you will do

• Understand a business problem and translate it into a realistic technical scope.

• Define the project architecture, milestones, and success criteria with your internship supervisor.

• Research possible approaches and explain their advantages, limitations, and trade-offs.

• Design and implement an AI-powered application, API, integration, or internal service.

• Integrate language models with approved data sources, APIs, and business tools.

• Work with technologies such as RAG, embeddings, vector search, structured outputs, tool calling, agents, or MCP when relevant.

• Evaluate the solution for accuracy, reliability, latency, cost, security, and privacy.

• Build tests for both traditional software behaviour and AI-specific behaviour.

• Use Git and follow normal software development and review practices.

• Document the architecture, setup, technical decisions, assumptions, and known limitations.

• Present progress regularly, collect feedback, and improve the solution iteratively.

• Prepare the solution for deployment in collaboration with the relevant technical teams.

• Deliver a final demonstration and recommendations for possible next steps.

Who we are looking for

• You are studying computer science, software engineering, applied IT, artificial intelligence, data, engineering, or another programme with a meaningful software development component.

• You have solid programming foundations in at least one modern language.

• You can design, write, debug, and test a small application or service.

• You understand core software engineering concepts such as modular code, error handling, configuration, dependencies, and basic automated testing.

• You have worked with HTTP or REST APIs, JSON, and Git.

• You understand the basic purpose of authentication and authorisation when connecting applications and services.

• You are interested in working with LLM APIs, prompts, context management, structured outputs, tool calling, and external data sources.

• You understand that an AI feature must be evaluated for reliability and failure cases rather than considered complete when the first demonstration works.

• You can investigate an unfamiliar technical subject, compare possible solutions, and explain what you learned.

• You are comfortable asking questions, receiving feedback, and be autonomous.

• You can communicate, collaborate, and document your work professionally in English.

Useful but not mandatory

Experience with any of the following would be valuable:

• OpenAI APIs, Azure AI, or another LLM API.

• GitHub Copilot or another AI-assisted development tool, combined with the ability to review and validate generated code.

• RAG, embeddings, vector databases, semantic search, or reranking.

• Tool calling, AI agents, or MCP.

• OAuth or another modern authentication mechanism.

• Building and consuming web APIs.

• Containers, CI/CD, application deployment, or monitoring.

• Automated evaluation of AI outputs.

• Data privacy, application security, prompt injection, or responsible AI.

• Developing a technical proof of concept and presenting its results.

What the internship offers

• Ownership of a substantial AI engineering project rather than a collection of isolated exercises.

• Regular guidance and technical feedback from an AI Engineer.

• Experience applying AI to concrete business problems in a SaaS environment.

• Collaboration with technical and non-technical colleagues across several teams.

• Exposure to the complete lifecycle of an AI solution: discovery, architecture, implementation, evaluation, deployment, documentation, and demonstration.

• The opportunity to experiment with emerging AI technologies while learning how to assess their practical limitations.

• The possibility of being considered for a future position at Mobilexpense following the internship, depending on your performance, mutual fit, company needs, and available opportunities. This should not be considered a guaranteed employment offer.