Project overview:

• Development of a mobile application that analyzes the emotional state of learners from written feedback and provides real-time conversational assistance adapted to their needs.
• Aim to combine sentiment analysis with an intelligent educational chatbot to improve motivation, reduce frustration and personalize learning support using modern NLP and LLMs.

Scope & responsibilities:

• Emotion and sentiment detection in learner feedback, including model selection, fine-tuning and evaluation on educational data.
• Build an adaptive conversational assistant (LLM-based chatbot) that uses detected emotional state to modulate responses and recommend coping strategies and learning tips.
• Implement a progress dashboard to monitor emotional engagement and well-being over time and surface actionable insights for learners and educators.

Technologies & tooling:

• Backend and ML: Python, HuggingFace Transformers, FastAPI, LangChain.
• Datastores and deployment: MongoDB / PostgreSQL, Docker; front-end: React / Next.js.
• Development workflow: Git / GitHub for version control and collaboration.

Required profile & conditions:

• Required profile: Software Engineer (internship level) with interest/experience in NLP, ML and full-stack development.
• Duration: 4 months; number of positions: 1 to 2 trainees.

How to apply:

• Apply via the company website: https://www.readdlytech.com or send your application by email to contact@readdlytech.com .
• In email applications use the subject line: "REF: Readdly-2026-02 Trainee Application" and include CV, motivations and relevant project or code samples.