AI Engineers

Key Responsibilities

Design and implement AI-driven conversational agents and autonomous workflows across Tutor, Coach, and Buddy products. Build scalable pipelines for LLM interaction, retrieval-augmented generation (RAG), and contextual memory.

• Integrate multi-modal capabilities (text, voice, image, video) into learning assistants.
• Collaborate with product, data, and backend teams to align agent behavior with learning outcomes and UX.
• Develop evaluation frameworks for accuracy, relevance, safety, and personalization.
• Fine-tune and optimize prompt chains, embeddings, and tool-use logic.
• Ensure compliance with ethical AI practices, hallucination reduction, and content moderation.
• Continuously research, test, and integrate new LLMs, APIs, and frameworks to enhance agent capabilities.
• Contribute to building reusable AI components and internal SDKs for faster agent development.
• Support A/B testing, telemetry integration, and performance analytics for deployed agents.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related field.
• 1–3 years of hands-on experience building AI applications, chatbots, or agentic systems.
• Strong understanding of LLM architectures, embeddings, vector databases, and RAG frameworks.
• Proficiency in Python and key AI/ML libraries (LangChain, LlamaIndex, OpenAI, Hugging Face).
• Experience with API integration, orchestration (FastAPI/Flask), and database management (MongoDB/Postgres).
• Familiarity with prompt engineering, system design for agents, and evaluation metrics.
• Excellent problem-solving, debugging, and documentation skills.
• Curiosity to explore emerging AI models, frameworks, and autonomous system designs.
• Tooling Proficiency (Must/Strongly Preferred)
• Frameworks: LangChain, LlamaIndex, OpenAI API, Hugging Face Transformers
• Programming: Python, TypeScript (nice to have)
• Databases: Pinecone, ChromaDB, FAISS, PostgreSQL
• APIs & Deployment: FastAPI, Flask, Docker
• Version Control: Git/GitHub
• Evaluation & Monitoring: Weights & Biases, PromptLayer, LangFuse
• Cloud & CI/CD: AWS, GCP, or Azure (preferred); GitHub Actions, Jenkins (nice to have)

Bonus Points

• Experience in building educational or learning-focused AI systems.
• Understanding of pedagogy, personalization models, and adaptive learning.
• Familiarity with voice agents, speech-to-text, and emotion detection APIs.
• Knowledge of graph-based memory or multi-agent frameworks.
• Experience conducting AI safety, fairness, and reliability evaluations.

WhatWe Offer

• Opportunity to shape the core AI systems of a fast-scaling EdTech company.
• Collaborative, research-driven, and mission-focused environment.
• Competitive compensation, ownership, and career growth opportunities.
• Flexible work setup and a chance to impact millions of learners globally.