Machine Learning Engineer — Applied AI

Company Description MENA Engineers connects global hiring teams with rigorously assessed technical professionals from across the Middle East and North Africa. The organization replaces crowded talent marketplaces with focused, evidence-based introductions based on a structured, human-led vetting process that evaluates identity, communication, practical skills, experience, and references. Candidate privacy is central, with profiles kept non-public and identifying details shared only after candidates approve specific opportunities. Current specializations include mobile engineering, backend and cloud, frontend development, QA automation, data and AI, and product design. MENA Engineers enables vetted professionals from the region to contribute to distributed teams worldwide.
Role Description This is a remote, contract role for a Machine Learning Engineer — Applied AI. In this role, you will design, implement, and optimize machine learning models and AI systems that address practical product and business challenges, from data preprocessing and feature engineering to model training, evaluation, and deployment. You will collaborate with cross-functional partners such as product managers, data engineers, and software developers to translate requirements into robust ML solutions and integrate models into production environments. Day-to-day responsibilities include experimenting with different algorithms and architectures, conducting statistical analyses, monitoring performance, and iterating on models based on real-world feedback and A/B testing results. You will also contribute to documentation, code reviews, and best practices for reproducible experimentation, scalable pipelines, and responsible AI.
Qualifications

• Strong foundation in Computer Science and Algorithms, including data structures, complexity analysis, and software engineering best practices.
• Applied expertise in Pattern Recognition and Neural Networks for building and refining models that solve real-world classification, prediction, and recommendation problems.
• Solid grasp of Statistics for experimental design, hypothesis testing, model evaluation, and performance monitoring.
• Hands-on experience with modern ML/AI frameworks and tools (e.g., Python, TensorFlow, PyTorch, scikit-learn) and version control systems.
• Experience deploying ML models into production (e.g., APIs, microservices, cloud platforms) and working with data pipelines or MLOps practices.
• Ability to work effectively in remote, distributed teams, with clear written and verbal communication and strong collaboration skills.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
• Familiarity with privacy, ethics, and fairness considerations in AI, as well as experience in data and AI domains relevant to global product teams, is an advantage.