We are looking for a skilled AI Developer based in Eastern Europe to join our distributed engineering team and help build our next‑generation, AI‑powered Learning Management System (LMS). You will work with modern technologies to develop scalable, secure, and high‑performance features that directly shape the future of digital learning.

This role is ideal for someone who enjoys working in a fast‑moving, international environment with strong engineering culture, autonomy, and clear product impact.

Responsibilities:

• Design, develop, and deploy machine learning models supporting personalization, recommendations, learner analytics, and adaptive pathways
• Develop high‑quality, maintainable, and scalable software components.
• Experiment with and integrate LLMs and NLP for content generation, interactive support, and automated feedback.
• Collaborate closely with global teams — Product, UX, AI/ML, Architecture, QA.
• Build new features for our AI‑enhanced LMS platform.
• Integrate APIs, third‑party services, and internal microservices.
• Contribute to code reviews, technical discussions, and architecture decisions.
• Write clean, well‑documented, and testable code.
• Optimize performance and ensure systems meet reliability, security, and data‑protection requirements.
• Troubleshoot issues, debug complex problems, and propose long‑term solutions.

Requirements:

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, or Statistics..pdf)
• Strong proficiency in Python and ML frameworks (scikit‑learn, PyTorch, etc.).
• Expertise in supervised & unsupervised learning, feature engineering, and model evaluation.
• Experience analyzing structured and unstructured data at scale.
• Proficiency in SQL and large‑dataset workflows.
• Ability to communicate complex ML concepts to diverse stakeholders.
• Experience in EdTech, LMS, or learning analytics environments is a big plus.
• Familiarity with LLMs, NLP, recommendation systems, or knowledge graphs..pdf)
• Experience deploying ML models to production (APIs, pipelines, real‑time inference).
• Exposure to AWS / GCP / Azure cloud ML environments is a plus.
• Experience with MLOps tools (CI/CD for ML, model monitoring, versioning).
• Understanding of learning science or instructional design principles.