Senior Machine Learning Engineer

at  Property Finder

Founded by Michael Lahyani in 2005 as a magazine (Al Bab World), Property Finder today is a single technology platform and brand across multiple countries in the MENA region. We offer the most advanced tools and best-in-class user experience for homeseekers, real estate brokers, and developers. Property Finder’s most recent valuation secures our status among the Middle East’s emerging unicorns, affirming a growth-oriented identity.
Over the years, we’ve expanded our operations to Bahrain, Egypt, Qatar, Saudi Arabia, and secured a strategic shareholding in Hepsiemlak, the leading property portal in Turkey. With over 600+ dedicated people in 6 regional offices, we facilitate more than 14 million monthly visits across our platforms, solidifying our position as a regional powerhouse in the proptech space.
As the pioneering portal for homeseekers in the region, we are on a mission to motivate and inspire people to live the life they deserve.

SUMMARY

We are seeking a highly skilled professional with expertise in Machine Learning Engineering (MLE/MLOps Level III or IV) and Data Science (Level II) to join our innovative AI & Data Science team at Property Finder. The ideal candidate will have a strong foundation in building scalable ML pipelines, deploying production-ready models, and applying advanced data science techniques to derive actionable insights that support strategic business initiatives. You will work on impactful projects in areas like predictive modeling, personalization, real-time AI systems, and scalable deployment pipelines, collaborating with cross-functional teams to drive innovation and operational efficiency.

DESIRED QUALIFICATIONS

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• At least 4+ years of experience in MLE/MLOps roles and 2+ years in data science roles.
• Proficiency in Python and ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Strong experience with MLOps tools (e.g., Kubernetes, Docker, MLflow).
• Advanced SQL skills for data extraction and manipulation.
• Hands-on experience with cloud platforms (AWS, GCP, Azure) and big data technologies (e.g., Spark).
• Expertise in CI/CD pipelines, version control, and model monitoring.
• Knowledge of statistical analysis and intermediate ML algorithms (e.g., decision trees, ensemble methods).
• Experience in supervised and unsupervised learning algorithms (e.g., decision trees, clustering, ensemble methods).
• Experience in advanced feature engineering and data preprocessing techniques.
• Familiarity with deep learning frameworks like TensorFlow or PyTorch.
• Working knowledge of statistical analysis, hypothesis testing, and experiment design.
• Proficiency in creating complex reports and dashboards for actionable insights.
• Strong problem-solving and analytical abilities.
• Effective communication skills to explain technical concepts to non-technical stakeholders.
• Ability to work collaboratively in cross-functional teams.

Machine Learning Engineering / MLOps (MLE/MLOps Level III or IV):

• Design and implement scalable ML pipelines, ensuring efficient model training, deployment, and monitoring.
• Optimize distributed training processes for large datasets and complex models.
• Automate workflows using CI/CD pipelines, workflow orchestration tools (e.g., Airflow, Kubeflow), and MLOps best practices.
• Develop robust systems for real-time inferencing and edge AI deployment.
• Monitor, troubleshoot, and improve production models for performance and reliability.

Data Science (Level II):

• Build and fine-tune ML models for business applications, including customer segmentation, personalization, and forecasting.
• Conduct advanced feature engineering and data wrangling to prepare high-quality datasets for modeling.
• Collaborate with stakeholders to understand business needs and translate them into data-driven solutions.
• Analyze large datasets to generate actionable insights and recommendations.
• Contribute to A/B testing and experimental designs to validate model performance.

Cross-Team Collaboration:

• Work closely with the Data Science, Engineering, and Product teams to align on project objectives and ensure smooth deployment of solutions.
• Partner with MLE/MLOps peers to integrate models into production systems and optimize end-to-end pipelines.