Technology Innovation Institute (TII) is a publicly funded research institute, based in Abu Dhabi, United Arab Emirates. It is home to a diverse community of leading scientists, engineers, mathematicians, and researchers from across the globe, transforming problems and roadblocks into pioneering research and technology prototypes that help move society ahead.
Artificial Intelligence Research Centre
The Artificial Intelligence Cross-Center Unit is the machine learning powerhouse of TII, working in close collaboration with our other research centers to harness the full benefits of AI across our projects – and drive innovation from new computing paradigms, designing and delivering new AI methodologies, technologies, solutions, and systems that address challenging issues across multiple sectors of the economy – from technology to healthcare, cybersecurity, and government, among others.
We incorporate core elements of intelligence (perception, sensing, planning, and language) in the ideation, design, and prototyping of next-generation systems with human-like intelligence. We build advanced AI computing and scalable AI-based software stacks and hardware systems to deliver significant enhancements in systems infrastructure.
Our AI researchers, scientists, and engineers collaborate to ensure innovative outcomes, from AI theory to AI technologies towards better intelligence.
The AI research center explores cutting-edge research problems as well as core ML infrastructure for distributed learning. As an AI Engineer, you will be a working in a close collaboration with the researcher to transform their proposals into concrete, optimized, and scalable implementation. You will be working on both the algorithmic side but also the infrastructure side. You will work closely with team to architect, build, and improve solutions for cloud and Edge ML. This role is physically located in Abu Dhabi, UAE.
Responsibilities
- Design and develop AI models
- Convert AI/ML models into APIs that other developers can use
- Perform statistical analysis on big data sets
- Manage project infrastructure and the development of AI models and projects
- Work with other colleagues to develop machine learning models
- Take offline models built by our researchers and turn them into a real machine learning production system
- Develop and deploy scalable tools and services to handle machine learning training and inference
- Identify and evaluate new technologies to improve performance, maintainability, and reliability of our machine learning systems
- Apply software engineering rigor and best practices to machine learning, e.g., automation.
- Communicate with different stakeholders in the eco-system
- Stay connected to industry standards and practices
- Contribute to technology transfers through the implementation of the research findings into proof of concept (PoC) according to the research needs
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback
Requirements
- 2+ years of experience in a related role
- Strong software engineering skills in complex, multi-language systems
- Strong proficiency in one or more common languages (Python)
- Comfort with Linux administration
- Experience working with cloud computing and database systems
- Good knowledge of common ML tools (e.g., Spark, TensorFlow, PyTorch)
Qualifications
- Master's degree in Computer Science, Electrical Engineering, or a related field
At TII, we help society to overcome its biggest hurdles through a rigorous approach to scientific discovery and inquiry, using state-of-the-art facilities and collaboration with leading international institutions. Our rigorous discovery and inquiry-based approach helps to forge new and disruptive breakthroughs in advanced materials, autonomous robotics, cryptography, digital security, directed energy, quantum computing and secure systems.
Key Skills
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- Posted
- May 29, 2025
- Type
- Full-time
- Level
- Mid-Senior
- Location
- Abu Dhabi Emirate
- Company
- Technology Innovation Institute
Industries
Categories
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