Deep Learning Engineer

Company Description Cretik is a Dubai-based software house, founded in 2020, specializing in bespoke software, mobile applications, UI/UX, websites, and e-commerce solutions. The company serves clients across Egypt, Saudi Arabia, and the broader MENA region, helping businesses establish a strong digital presence. Cretik’s professional team focuses on designing intuitive, visually compelling digital products that align with client goals and user needs. By combining modern technologies with thoughtful design, Cretik enables organizations to exceed industry standards and gain a competitive edge. The company values innovation, collaboration, and continuous improvement in delivering high-quality digital solutions.

Role Description The Deep Learning Engineer role is a full-time, remote position focused on building and optimizing machine learning and deep learning solutions for Cretik’s digital products and client projects. On a day-to-day basis, the engineer will design, implement, and experiment with neural network architectures; preprocess and manage large datasets; and develop robust training, validation, and deployment pipelines. The role involves collaborating with product managers, software engineers, and UI/UX designers to integrate models into production systems and ensure they meet performance, scalability, and reliability requirements. The engineer will also monitor model performance, refine algorithms, and explore new techniques and frameworks to improve accuracy and efficiency. Documentation, code reviews, and adherence to best practices in software engineering and ML operations are integral parts of the role.

Qualifications

• Strong foundation in machine learning and deep learning concepts, including supervised/unsupervised learning, model evaluation, and optimization techniques.
• Experience with deep learning frameworks and tools such as TensorFlow, PyTorch, Keras, or similar libraries.
• Proficiency in programming languages commonly used in ML development, such as Python, and familiarity with relevant libraries (NumPy, Pandas, scikit-learn).
• Skills in data preprocessing, feature engineering, and working with structured and unstructured datasets.
• Knowledge of deploying ML models to production environments, including APIs, microservices, or cloud-based solutions (e.g., AWS, GCP, Azure).
• Understanding of software engineering best practices, including version control (Git), testing, and code documentation.
• Ability to collaborate in cross-functional teams and communicate technical concepts clearly to non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
• Experience with computer vision, natural language processing, or recommendation systems is a plus.
• Familiarity with MLOps practices, model monitoring, and performance tuning is advantageous.