- Solution Architecture: Collaborate with cross-functional teams to define the technical architecture and infrastructure required for the adaptive learning solution.
- Algorithm Development: Develop and implement machine learning algorithms to analyze 120,000 digital learning objects aligned with national educational standards and assess students' results from engaging with these objects via assessment items. Utilize this data to recommend personalized learning paths.
- Learning Progression Mapping: Leverage a learning progression map that describes the hierarchy and relationships between learning standards and sub-standards to determine the content and learning skills that should be delivered next to the student.
- Content Customization: Customize existing learning objects and assessment items based on student preferences and learning style, ensuring a tailored learning experience.
- Content Generation: Utilize large language models and generative AI solutions to create new content themes and contexts on the fly while maintaining the same key ideas, concepts, and skills for evaluation.
- Data Collection and Preprocessing: Design data pipelines to collect and preprocess large volumes of student data, learning objects, and assessment results, ensuring data quality and privacy compliance.
- Model Training and Evaluation: Train, validate, and optimize machine learning models for skill assessment, content recommendation, and student progress tracking using granular learning analytics data.
- User Interface Integration: Collaborate with front-end developers to integrate AI-driven features into the user interface, providing a seamless and personalized learning experience.
- Monitoring and Optimization: Continuously monitor the performance of the adaptive learning solution, identifying areas for improvement and implementing enhancements based on the analysis of granular learning analytics data.
- Data Analytics and Reporting: Develop dashboards and reporting tools to track student progress, mastery, and other key metrics, providing insights to educators and administrators.
- Documentation: Maintain comprehensive documentation of the AI/ML architecture, algorithms, and processes for knowledge sharing and future reference.
- Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
- Proven experience in designing and implementing machine learning solutions, preferably in the education domain.
- Expertise in data analysis and feature engineering, particularly with large-scale educational datasets.
- Proficiency in machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Strong programming skills in languages such as Python, Java, or C++.
- Experience with large language models for content generation (e.g., GPT-3, GPT-4).
- Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and distributed computing.
- Excellent problem-solving skills and the ability to work independently and in a team.
- Effective communication skills to collaborate with cross-functional teams and stakeholders.
- Commitment to staying updated with the latest developments in AI/ML and education technology.
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Senior Artificial Intelligence/Machine Learning Engineer - Bengaluru, India - Matellio
Description
Job Description :
We are seeking a highly skilled and motivated AI/ML Engineer to lead the development of an adaptive learning solution that leverages machine learning to personalize education experiences for students. This role will be crucial in designing and implementing the technology stack required to create a dynamic and effective learning platform. As part of our team, you will have the opportunity to shape the future of education and make a significant impact on the learning outcomes of our students.
Responsibilities :