Mid-Level AI Engineer

About the role

We are seeking a Mid-Level AI Engineer to design, build, evaluate, and deploy AI‑powered solutions for our education platform. The role focuses on natural language processing, large language model (LLM) applications, retrieval‑augmented generation, and voice technologies to enhance tutoring, transcription, quiz generation, and Arabic language support.

Key responsibilities

• Design and develop AI applications using LLMs, NLP techniques, and production‑ready Python services.
• Build conversational AI systems, chatbots, AI assistants, and education‑focused copilots.
• Develop and optimize voice AI pipelines, including Speech‑to‑Text, Text‑to‑Speech, and voice interaction flows.
• Implement Retrieval‑Augmented Generation (RAG) architectures with embeddings and vector databases.
• Fine‑tune, evaluate, and monitor language models for production use cases.
• Create REST APIs and scalable backend services for AI‑powered features.
• Integrate OpenAI APIs, Hugging Face models, LangChain/LlamaIndex, and other AI services.
• Work on Arabic NLP use cases, handling dialects and learner support.
• Collaborate with product, backend, frontend, data, and training teams.
• Monitor AI quality, latency, cost, hallucination risk, and reliability in production.

Required profile

• 2‑3 years of hands‑on experience in AI, ML, NLP, or LLM application development.
• Strong Python programming skills and ability to build clean, maintainable services.
• Experience with Arabic NLP and related language use cases.
• Proven ability to work with OpenAI APIs, LangChain or LlamaIndex, and Hugging Face Transformers.
• Familiarity with prompt engineering, RAG systems, embeddings, and retrieval evaluation.
• Experience deploying AI systems on AWS, Azure, or GCP.
• Background in EdTech, learning platforms, tutoring systems, or Arabic content products is a plus.

Required skills

• Python
• OpenAI APIs
• LangChain
• LlamaIndex
• Hugging Face Transformers
• Prompt engineering
• Retrieval‑Augmented Generation (RAG)
• Embeddings
• Vector databases (Pinecone, Weaviate, ChromaDB)
• REST API development
• Git
• Voice AI (Speech‑to‑Text, Text‑to‑Speech)
• AWS
• Azure
• GCP
• Arabic NLP