AI/ML Intern

Fluency in English is a must

· MUST submit a Resume in English

· Working hours: EST

We are looking for a motivated AI/ML Intern to join our Client’s team as part of their graduation project (PFE).

This internship is designed for students who want to apply their academic knowledge to a real-world Artificial Intelligence or Machine Learning problem. The intern will work on a defined AI/ML project from idea to prototype, with guidance from experienced technical team members.

You will have the opportunity to research, experiment, build, evaluate, and present an AI solution while gaining practical exposure to modern AI technologies and engineering practices.

As an AI/ML Intern, you will:

· Take ownership of an assigned graduation project in Artificial Intelligence, Machine Learning, or Generative AI.

· Work with technical mentors to understand the problem, define objectives, and establish a clear project plan.

· Research relevant models, algorithms, frameworks, and technologies.

· Collect, clean, prepare, and analyze data required for the project.

· Design and implement an AI/ML solution based on the identified problem.

· Build prototypes and conduct experiments to compare different approaches.

· Evaluate model or application performance using appropriate technical and business-oriented metrics.

· Analyze results, identify limitations, and iterate on the solution.

· Document the technical approach, experiments, results, and conclusions.

· Present project progress and findings to technical mentors and stakeholders.

· Prepare a final project demonstration and technical documentation.

Depending on the project, the intern may work in one or more of the following areas:

Generative AI & Large Language Models

· Large Language Models (LLMs)

· Prompt engineering

· Retrieval-Augmented Generation (RAG)

· AI-powered assistants and chatbots

· Document understanding and question answering

· AI agents and workflow automation

· Model evaluation and hallucination mitigation

· Open-source and commercial AI models

Machine Learning & Deep Learning

· Supervised and unsupervised learning

· Natural Language Processing (NLP)

· Computer Vision

· Classification and prediction

· Recommendation systems

· Forecasting

· Neural networks and transformer-based models

· Model training, validation, and evaluation

AI Engineering

· Python-based AI applications

· Data preparation and processing

· AI/ML APIs and integrations

· Experimentation and model evaluation

· AI application development

· Introduction to deployment and monitoring

· Currently pursuing a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical field.

· The internship must be part of a graduation/final-year project (PFE).

· Good understanding of fundamental Machine Learning and/or Artificial Intelligence concepts.

· Strong interest in Artificial Intelligence and emerging technologies.

· Good programming skills in Python.

· Familiarity with data structures, algorithms, and basic software development concepts.

· Academic or personal project experience in AI, Machine Learning, Data Science, or software development.

· Ability to research technical topics independently and learn new technologies quickly.

· Strong analytical and problem-solving skills.

· Good written and verbal communication skills.

· Ability to work independently while collaborating with technical mentors and team members.

Exposure to one or more of the following is a plus:

· Python

· Scikit-learn

· PyTorch or TensorFlow

· Hugging Face

· NLP

· Computer Vision

· Generative AI

· Large Language Models

· RAG

· OpenAI, Claude, Gemini, or other LLM platforms

· LangChain or LangGraph

· SQL

· REST APIs

· Git/GitHub

· Cloud platforms such as AWS, Azure, or Google Cloud

By the end of the internship, the intern is expected to have:

· Defined and documented the problem being addressed.

· Researched and justified the selected technical approach.

· Developed a functional AI/ML prototype or solution.

· Conducted experiments and evaluated the solution.

· Documented the methodology, results, limitations, and potential improvements.

· Presented the final solution and demonstrated the working prototype.