Mid-Level AI Engineer (NLP & Voice AI)

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 focuses on Natural Language Processing, large language models, Retrieval‑Augmented Generation and voice technologies, with a special emphasis on Arabic language capabilities.

Key responsibilities

• Design and implement AI applications using LLMs, NLP techniques and production‑ready Python services.
• Develop conversational agents, chat‑bots and education‑focused copilots.
• Build and optimise voice AI pipelines, including Speech‑to‑Text, Text‑to‑Speech and interactive voice flows.
• Implement RAG architectures with embeddings, vector databases and curated knowledge sources.
• 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 voice AI workflows.
• Proven track record of deploying AI systems on cloud platforms (AWS, Azure or GCP).
• Familiarity with collaborative development tools such as Git.

Required skills

• Python
• Natural Language Processing (NLP)
• Large Language Models (LLM) frameworks
• OpenAI APIs
• LangChain / LlamaIndex
• Hugging Face Transformers
• Prompt engineering and RAG
• Embeddings and vector databases (Pinecone, Weaviate, ChromaDB)
• REST API development
• Voice AI (Speech‑to‑Text, Text‑to‑Speech)
• Arabic NLP
• Cloud platforms (AWS, Azure, GCP)
• Model fine‑tuning and evaluation
• AI observability and cost optimisation