Technology Innovation Institute
About the job
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 Cross-Center Unit
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.
We are seeking a skilled and highly motivated DevOps Engineer to join our team. As a DevOps Engineer, you will play a vital role in designing, implementing, and maintaining our infrastructure and development processes. You will collaborate closely with cross-functional teams to ensure the efficient and reliable delivery of our software products. If you are passionate about automation, scalability, and continuous integration/continuous deployment (CI/CD), this is an exciting opportunity to make a significant impact in a fast-paced environment.
- Design and develop AI models to meet project requirements
- 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
- Design, develop, and maintain our infrastructure and deployment pipelines to support software development and release processes
- Implement and enhance automation tools for building, testing, and deploying software applications
- Collaborate with software developers, system administrators, and other stakeholders to optimize software delivery and infrastructure performance
- Monitor and troubleshoot production systems to ensure high availability, reliability, and performance
- Develop and maintain documentation related to infrastructure, deployment processes, and system configurations
- Stay up-to-date with industry best practices and emerging technologies in DevOps, cloud computing, and automation
- 3+ years of experience in the field.
- Proven experience as a DevOps Engineer or in a similar role.
- Strong knowledge of Linux/Unix operating systems and scripting languages (e.g., Shell, Python, Ruby).
- Proficiency in cloud computing platforms (e.g., AWS, Azure, Google Cloud) and related services (e.g., EC2, S3, RDS).
- Hands-on experience with configuration management and infrastructure-as-code tools (
- Experience with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools.
- Familiarity with CI/CD concepts and tools (e.g., Jenkins, GitLab CI/CD, CircleCI).
- Solid understanding of networking, security, and monitoring principles in a distributed
- Strong software engineering skills in complex, multi-language systems
- Strong proficiency in one or more common languages (e.g., C++, Java, Python)
- Comfort with Linux administration
- Experience working with cloud computing and database systems
- Good knowledge of common ML tools (e.g., Spark, TensorFlow, PyTorch)
- An MSc degree in Software engineering, machine learning or related field with 3+ years’ experience as in a related role