About the role

We are looking for an intern to join our Artificial Intelligence Center of Excellence within the Digital Hub. We are seeking a curious, hands-on learner who is interested in how AI can support our business areas (Construction, Highways, Airports, Energy, Digital Infrastructures and Headquarters). The primary focus of the internship will be agentic AI, Generative AI applications and AI-enabled software solutions, while the position will remain open to candidates interested in data science, machine learning and computer vision.

The selected candidate will work alongside Data Scientists and AI Engineers, contributing to the design, development and integration of intelligent agents and AI-powered applications. The role is particularly suited to a student who enjoys building software with Python, connecting models to APIs and enterprise systems, experimenting with agent orchestration frameworks, and turning Generative AI ideas into working prototypes. Depending on the candidate’s interests and project needs, the internship may also include machine learning, data science or computer vision activities.

The ideal candidate should have a strong capacity for learning, be results-oriented, and possess solid teamwork skills.

Key Responsibilities

• Support the design and development of Generative AI applications and agentic solutions, from early experimentation to working prototypes.

• Develop and test Python components, tools and services that enable agents to interact with data sources, APIs and business systems.

• Experiment with agent orchestration frameworks, LLM APIs, prompt design, tool use, Retrieval-Augmented Generation (RAG) and related techniques.

• Assist in the initial analysis of business use cases, technical requirements and available data.

• Conduct research and small technical experiments to assess emerging AI models, frameworks and development patterns.

• Collaborate with Data Scientists, AI Engineers and other technical teams to integrate AI capabilities into scalable and maintainable digital products.

• Contribute, where relevant, to machine learning, deep learning, data science or computer vision initiatives.

• Document solutions and help communicate results to business teams and end users.

• Actively participate in events and knowledge-sharing activities within the Digital Hub and the broader Technology & Innovation area.

Qualifications

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Telecommunications, Mathematics, Physics or a related technical field.

• Good foundation in Python and software development fundamentals, including modular code, debugging, testing and basic data structures.

• Familiarity with REST APIs, JSON and the integration of external services, models or data sources.

• Academic, personal or internship project experience building a Generative AI application or prototype using LLM APIs; professional experience is not required.

• Familiarity with at least one agent or LLM application framework, such as Microsoft Agent Framework, Semantic Kernel, LangGraph, LangChain or LlamaIndex, and an interest in learning new frameworks as the ecosystem evolves.

• Basic understanding of key Generative AI concepts, such as prompt design, embeddings, vector search, Retrieval-Augmented Generation (RAG), tool use and agent orchestration.

• Basic knowledge of Git and collaborative software development practices.

• Basic understanding of the Azure AI ecosystem or another cloud platform is valued but not required.

• Knowledge of machine learning, deep learning or computer vision, and familiarity with libraries such as scikit-learn, PyTorch or TensorFlow, will be considered an advantage.

• Strong analytical and problem-solving skills, curiosity, initiative and a willingness to learn through hands-on experimentation.

• Ability to work collaboratively, communicate clearly and document technical work for both technical and non-technical audiences.

• High level of English; a minimum C1 level is preferred.