Generative AI Engineer

About LearningBIX LearningBIX is committed to inspiring the innovators of tomorrow by providing hands-on learning solutions that empower students to bring their creative ideas to life. The company focuses on strengthening STEM education through technology-driven learning tools, practical innovation, and engaging educational experiences. We are building AI-powered educational solutions for students and teachers, including offline AI tutors, bilingual learning systems.

Role Overview We are looking for an Artificial Intelligence Engineer to build and deploy an offline AI Tutor platform for school education. This role will focus on designing and implementing RAG-based AI systems, quantized LLM deployment on edge devices, and Hindi-English educational AI workflows for student and teacher applications. The ideal candidate should be comfortable working across LLMs, LangChain pipelines, vector databases, embeddings, model optimization, local inference, and educational AI products.

Job Location: Jaipur, Rajasthan (On-site) Key Responsibilities Design and develop an offline AI tutor system for students and teachers using LLMs, RAG, and LangChain-based workflows. Build retrieval-augmented generation pipelines using local document sources such as textbooks, notes, question banks, and curriculum content. LangChain provides components for retrieval pipelines, document loading, chunking, and vector-store integration that fit this kind of workflow.

Integrate quantized GGUF models with local inference runtimes such as llama.cpp. llama.cpp supports GGUF models, multiple quantization levels, local serving, and CPU/GPU hybrid inference for running models efficiently on local hardware. Optimize AI pipelines for edge and offline deployment, ensuring reliable inference on resource-constrained systems. Implement multilingual semantic search using embedding models such as paraphrase-multilingual-MiniLM-L12-v2, which supports multilingual text embeddings across many languages.

Build and manage local vector stores using FAISS or similar retrieval systems for similarity search and contextual grounding. FAISS is designed for efficient similarity search over dense vectors and is commonly used in RAG systems. Preferred Skills Experience with edge AI deployment and offline AI systems Familiarity with local inference servers and OpenAI-compatible local APIs. llama.cpp includes a local API server option for serving models through HTTP.

Experience with Hindi-English multilingual NLP Experience in education AI, EdTech, tutoring systems, assessment systems, or curriculum-based AI tools Familiarity with prompt engineering, LoRA/QLoRA fine-tuning, and evaluation pipelines Experience with semantic search, embeddings, and local knowledge-base systems Educational Qualifications Bachelor’s or Master’s degree in Artificial Intelligence, Computer Science, Data Science, Machine Learning, or a related field Why Join LearningBIX Work on meaningful AI products for education Build real-world offline AI systems that can impact schools and students Collaborate with an innovation-driven team focused on STEM and future-ready learning Be part of a mission-led company using technology to improve education outcomes