Mid-Level AI Engineer

About the role

We are seeking a Mid‑Level AI Engineer to design, build and deploy AI‑powered solutions that enhance education, automation and user engagement. The role will focus on natural language processing, large language model applications, retrieval‑augmented generation and voice technologies, with a strong emphasis on Arabic language capabilities.

Key responsibilities

• Design and implement production‑ready Python services for LLM‑based NLP applications.
• Develop conversational agents, chatbots and education‑focused AI copilots.
• Build and optimise voice AI pipelines, including speech‑to‑text and text‑to‑speech workflows.
• Implement Retrieval‑Augmented Generation architectures using embeddings and vector databases.
• Fine‑tune, evaluate and monitor language models for production use cases.
• Create REST APIs and scalable backend services for AI 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 deliver clean, maintainable services.
• Experience with Arabic NLP and related language challenges.
• Proven track record of deploying AI systems on cloud platforms (AWS, Azure or GCP).
• Familiarity with AI observability, model evaluation, guardrails and cost optimisation.
• Background in EdTech, learning platforms or Arabic content products is a plus.

Required skills

• Python
• Natural Language Processing (NLP)
• Large Language Model frameworks (e.g., Hugging Face Transformers)
• OpenAI API
• LangChain / LlamaIndex
• Prompt engineering and Retrieval‑Augmented Generation
• Embeddings and vector databases (Pinecone, Weaviate, ChromaDB)
• REST API development
• Git version control
• Voice AI (Speech‑to‑Text, Text‑to‑Speech)
• Cloud platforms: AWS, Azure, GCP
• Model fine‑tuning and evaluation
• AI observability and cost optimisation
• Arabic NLP