Machine Learning Engineer - Riyadh

About Mozn

Mozn is a rapidly growing technology firm revolutionizing the field of Artificial Intelligence and Data Science headquartered in Riyadh, Saudi Arabia and it’s working to realize Vision 2030 with a proven track record of excellence in supporting and growing the tech ecosystem in Saudi Arabia and the GCC region. Mozn is the trusted AI technology partner for some of the largest government organizations, as well as many large corporations and startups.

We are in an exciting stage of scaling the company to provide AI-powered products and solutions both locally and globally that ensure the growth and prosperity of our digital humanity. It is an exciting time to work in the field of AI to create a long-lasting impact.

About The Role

We are seeking a talented Machine Learning Engineer to join our growing team. As a Machine Learning Engineer, you will play a pivotal role in optimizing ML models for efficient training and inference, deploying deep learning models in specialized hardware for inference usage, monitoring the performance and latency of deployed models, and maintaining ML infrastructure.

What You'll Do

• Designing, building, supporting, and scaling our cloud and/or our on-premise ML infrastructure.
• Deploying deep learning models in production environments and optimizing their performance for inference on either GPU or CPU.
• Maintaining our infrastructure (on-prem and cloud) and preparing it for training and inference purposes.
• Monitoring deployed ML models for their performance, latency, and throughput using automated tools for monitoring and reporting.
• Evaluating and improving data science processes, identifying opportunities for automation, efficiency, and scalability.
• Collaborating with other teams, including product managers, data scientists, software engineers, data annotators, and business stakeholders, to ensure successful deployments of ML models.
• Staying up to date with the latest trends and advancements in ML engineering, ML models, and applying this knowledge to enhance the team’s capabilities.
• Exploring and learning new technologies that can complement or replace our current stack to improve it.
  
  

Qualifications

• Bachelor's or Master's degree in Computer Science or a related field.
• +2 years of experience in a similar role.
• Proficiency in Arabic language (Native Arabic speaker) is a must.
• Proficiency in one or more programming languages (e.g., Python, C, C++) with the ability to learn new languages.
• Experience with relational databases, including SQL queries, database definition, and schema design.
• Experience with deploying Deep learning frameworks (e.g. Tensorflow, Pytorch, Onnx) in production environments using inference frameworks (e.g. Nvidia Triton, TFXServing, TorchServe).
• Uphold best practices and principles around clean code, version control, testing, continuous integration and continuous deployment.
• Effective communication skills to convey technical solutions to end-users.
• Experience with monitoring ML models and reporting tools (e.g. grafana, and/or promethues).
• Experience with containerization technologies (e.g. Docker) is highly preferred.
• Experience with distributed computing systems is a plus.
• Experience with cloud platforms (e.g. AWS, GCP, OCI) is a plus.
• Knowledge of big data platforms like kafka, hadoop, and spark is a plus.
  
  

Benefits

Why Mozn?

• You will be at the forefront of an exciting time for the Middle East, joining a high-growth rocket-ship in an exciting space.
• You will be given a lot of responsibility and trust. We believe that the best results come when the people responsible for a function are given the freedom to do what they think is best.
• The fundamentals will be taken care of: competitive compensation, top-tier health insurance, and an enabling culture so that you can focus on what you do best.
• You will enjoy a fun and dynamic workplace working alongside some of the greatest minds in AI.
• We believe strength lies in difference, embracing all for who they are and empowered to be the best version of themselves.