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.