AI/ML Software Engineer

Responsibilities:

Solution Architecture:

• Collaborate with cross-functional teams to define the technical architecture and infrastructure required for the adaptive learning solution.
• Collaborate with stakeholders to translate business requirements into technical solutions.

Algorithm Development:

• Develop and implement machine learning algorithms for analyzing digital learning objects and recommending personalized learning paths.
• Design algorithms for skill assessment, content recommendation, and student progress tracking using granular learning analytics data.

Learning Progression Mapping:

• Utilize learning progression maps to guide the delivery of relevant content and skills to students.

Content Customization & Generation:

• Customize existing learning objects and assessments based on student preferences and learning styles.
• Utilize large language models and generative AI solutions to create new content themes while maintaining key ideas and skills.

Data Collection and Preprocessing:

• Design data pipelines to collect and preprocess large volumes of student data, ensuring data quality and privacy compliance.
• Perform feature engineering on large-scale educational datasets.

Model Training and Evaluation:

• Train, validate, and optimize machine learning models for skill assessment, content recommendation, and student progress tracking.

User Interface Integration:

• Collaborate with front-end developers to integrate AI-driven features into the user interface.

Monitoring and Optimization:

• Continuously monitor and enhance the adaptive learning solution based on granular learning analytics data.
• Optimize AI/ML solutions in production environments.

Data Analytics and Reporting:

• Develop dashboards and reporting tools to track student progress and provide insights to educators and administrators.

Documentation:

• Maintain comprehensive documentation of the AI/ML architecture, algorithms, and processes.

Required Skills and Experience:

Technical Expertise:

• Strong software engineering background with expertise in full-stack development, including front-end, back-end, and database technologies.
• Proficiency in machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, or similar.
• Strong programming skills in languages such as Python, Java, or C++.
• Experience with large language models (e.g., GPT-3, GPT-4) and frameworks like LangChain/HuggingFace, LangFlow, or FlowiseAI.
• Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and distributed computing.
• Familiarity with open-source frameworks and tools (FoSS) and their copyleft implications.

Machine Learning Knowledge:

• Understanding of machine learning concepts, including supervised, unsupervised, deep-learning, transformers, and reinforcement learning.
• Proven experience in deep learning, machine learning model selection, and benchmarking.

Mathematics and Statistics:

• Strong foundation in mathematics and statistics.

Analytical Skills:

• Excellent problem-solving and critical-thinking skills.
• Strong analytical and data-driven decision-making skills.

Communication & Teamwork:

• Strong communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
• Effective communication skills to present technical concepts to non-technical stakeholders.

Continuous Learning:

• Continuous learning mindset and a passion for staying up-to-date with the latest advancements in AI/ML and education technology.

Fast-Paced Environment:

• Ability to work in a fast-paced, research-oriented environment.

Preferred Qualifications:

Cloud-based AI/ML:

• Hands-on experience with cloud-based AI/ML services, such as AWS SageMaker, Google Cloud AI, or Microsoft Azure ML.

Web Development:

• Working knowledge of NodeJS frameworks such as ReactJS, VueJS, or Svelte.

Agile & DevOps:

• Familiarity with agile software development methodologies and DevOps practices.