AI ML Engineering Internship

MAIN DUTIES

• Assist in well-defined pieces of work around research & development. Contribute to model and algorithm design using state of the art AI techniques such as large-language-models (LLM).
• Contribute to rigorous evaluation of AI models and systems. Choose the appropriate metrics for the assigned task.
• Support the setup of reproducible experiments in Python, following best processes for experimental tracking
• Assist with tasks like data cleaning, feature engineering, and building baseline AI models.
• Contribute to documentation by maintaining concise experiment logs, clear code comments, and short write-ups.
• Help the team stay up to date by reading recent AI-related papers or exploring new tools, and summarizing key insights.
• Participate in internal demos, team discussions, and code reviews to gain experience and contribute where possible.
• Success Indicators
• Learning Curve & Initiative: Willingness to learn. Demonstrate skill growth and ownership of small tasks from start to finish.
• Code Quality & Reproducibility: Well-structured, testable Python code and clearly documented experiments.
• Collaboration: Timely communication of progress and blockers. Thorough documentation of deliverables.
• Impactful Contributions: Measurable improvements in model accuracy, runtime efficiency, or tooling.

REQUIREMENTS

• Education
• Completed or actively pursuing a BSc or MSc in Computer Science, Data Science, AI, or a related field (final-year undergraduates welcome).
• Technical Foundation
• Coursework or personal projects in AI, machine learning or NLP, solid Python fundamentals, hands on experience with LLMs
• Tools & Frameworks
• Familiarity with at least one AI/ML library such as scikit-learn, TensorFlow, or PyTorch, experience with Git. Basic knowledge of Docker or cloud services is a plus.
• Soft Skills
• Clear written and verbal English communication, curiosity, problem-solving attitude, and willingness to ask questions.