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 TransformersProgramming : Python, TypeScript (nice to have)Databases : Pinecone, ChromaDB, FAISS, PostgreSQLAPIs & Deployment : FastAPI, Flask, DockerVersion Control : Git / GitHubEvaluation & Monitoring : Weights & Biases, PromptLayer, LangFuseCloud & 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.