AI Engineer (Hybrid)

Key Responsibilities

• Design and develop AI-powered solutions for use cases involving natural language, speech, images, documents, and structured data.
• Build and improve systems for search, question answering, summarization, content generation, classification, extraction, automation, and intelligent assistants.
• Work with modern AI models, including large language models, speech models, image models, OCR models, and multimodal models.
• Develop AI pipelines that can process different types of input, such as text, audio, images, scanned documents, PDFs, forms, and tables.
• Evaluate AI models based on quality, speed, cost, reliability, scalability, and business fit.
• Deploy AI services into production environments and integrate them with web, mobile, backend, and internal systems.
• Improve AI system performance through testing, monitoring, optimization, and continuous iteration.
• Collaborate with product, engineering, design, and business teams to deliver practical AI features.
• Mentor engineers, review technical decisions, and contribute to AI architecture and best practices.

Required Qualifications

• Hands-on experience building AI systems using modern machine learning and deep learning models.
• Strong programming skills, especially in Python.
• Experience developing APIs, backend services, and production-ready software.
• Good understanding of AI model evaluation, data preparation, system design, and deployment.
• Experience working with servers, containers, databases, and version control.
• Ability to research, compare, and select suitable AI models and tools for different business needs.

Preferred Qualifications

• Experience with large language models and retrieval-based AI systems.
• Experience with speech-to-text and text-to-speech systems.
• Experience with image processing, computer vision, OCR, and document understanding.
• Experience with multimodal AI systems that combine text, audio, images, and documents.
• Experience deploying AI models on GPUs or optimized inference environments.
• Experience with AI monitoring, evaluation, and quality improvement workflows.
• Experience working with Arabic language AI, Arabic speech, or regional dialects is a plus.

Key Skills

• Artificial intelligence and machine learning
• Large language models
• Natural language processing
• Computer vision and image understanding
• Speech-to-text and text-to-speech
• OCR and document intelligence
• Multimodal AI
• Backend development and APIs
• Model evaluation and optimization
• Production deployment
• Cloud, Docker, Linux, and Git

Success Criteria

The successful candidate will be able to build AI features that are useful, reliable, scalable, and ready for real users. They will help the team choose the right models, design strong AI pipelines, reduce errors, improve quality, and turn AI experiments into production products.