The Position
We're seeking an AI specialist to join our multidisciplinary team in developing cutting-edge AI solutions for education. The role focuses on fine-tuning and implementing AI models that enhance learning and teaching effectiveness through our educational platforms.
Key Responsibilities:
- AI Solution Design: Conceptualize and develop innovative AI solutions tailored for educational applications, enhancing learning and teaching experiences.
- LLM Fine-Tuning: Customize and fine-tune Large Language Models to address specific educational use cases, ensuring relevance and effectiveness.
- RAG & Knowledge Systems: Design, implement, and optimize Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured educational content to improve factual accuracy and contextual relevance.
- Agentic AI Systems: Design, implement, and evaluate AI agent–based architectures (multi-agent systems, tool-using agents, autonomous workflows) for complex educational tasks.
- Model Context & Tool Integration: Implement and manage Model Context Protocol (MCP)–based integrations to enable secure, scalable, and standardized access to tools, data sources, and services for LLM-powered systems.
- AI Pipeline Development: Create and optimize robust AI pipelines for processing, retrieval, orchestration, and analysis of educational data, ensuring efficiency and scalability.
- ML Infrastructure Management: Build, deploy, and maintain scalable machine learning infrastructure to support ongoing and future AI initiatives.
- Collaborative Research: Work closely with the research team to analyze data on learning effectiveness, leveraging insights to inform AI development.
- Technical Documentation: Document detailed technical specifications, algorithms, architectures, and workflows, and maintain a clean, organized codebase.
- Continuous Improvement: Stay updated with advancements in LLMs, agentic frameworks, MCP standards, and EdTech research, integrating new methodologies into production systems.
- Cross-Functional Collaboration: Partner with product managers, educators, and other stakeholders to align AI initiatives with educational goals and user needs.
Technical Requirements:
Education:
- Master's or doctorate degree in Computer Science, AI, or related field from top universities
Essential Technical Skills:
- Machine Learning & LLM Expertise: Proven experience in ML model development, deployment, and extensive hands-on experience with Large Language Model (LLM) fine-tuning.
- RAG Implementation: Strong experience building and evaluating Retrieval-Augmented Generation systems, including embedding strategies, vector databases, chunking, reranking, and retrieval optimization.
- Agentic Framework Familiarity: Practical experience or strong familiarity with AI agentic frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar), including tool orchestration, memory, and multi-step reasoning workflows.
- MCP Familiarity: Experience or working knowledge of Model Context Protocol (MCP) for standardized LLM access to external tools, services, and data sources.
- Programming Proficiency: Advanced skills in Python, including writing efficient, maintainable, and well-documented code.
- ML Frameworks: Proficiency with frameworks such as PyTorch and TensorFlow, with experience deploying and scaling ML models.
- Data & Storage: Strong knowledge of SQL and NoSQL databases, vector databases, and data pipelines for AI-driven applications.
- Version Control: Proficient in Git for collaborative software development.
- API Development: Experience building RESTful APIs for integrating AI services into production systems.
- Containerization: Knowledge of Docker for reproducible development and production environments.
- Cloud Computing: Understanding of cloud platforms and services supporting AI and ML workloads (if applicable).
Desired Skills:
- EdTech Industry Experience: Previous experience working within the educational technology sector, understanding its unique challenges and opportunities.
- Cloud Service Proficiency: Hands-on experience with major cloud service providers such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.
- CI/CD Pipeline Management: Experience in setting up and managing Continuous Integration and Continuous Deployment pipelines to streamline development processes.
- Evaluation & Safety: Familiarity with LLM evaluation, hallucination mitigation, guardrails, and responsible AI practices.
- Analytical Thinking: Strong analytical and problem-solving skills, with the ability to approach complex challenges methodically.
- Communication Skills: Excellent written and verbal communication abilities, capable of articulating complex technical concepts to diverse audiences.
- Project Management: Proven experience in managing projects, ensuring timely delivery and adherence to quality standards.
- Agile Methodologies: Familiarity with Agile development environments, including experience working i