Applied AI/ML Engineer - Gaming (Saudi National's)

Tech StackPython, scikit-learn, TensorFlow or PyTorch, Pandas, NumPy, SQL/NoSQL databases (PostgreSQL, BigQuery, MongoDB), ETL pipelines (Airflow, Prefect), cloud storage (S3, GCS), Docker, Kubernetes, FastAPI/Flask, MLflow/Kubeflow/Vertex AI, AWS/GCP/Azure, GitWhat You’ll DoYou’ll design, develop, and deploy machine learning models that enhance the player experience for a popular game, including churn prediction, personalization, user engagement optimization, and fraud detection. You’ll build and maintain data pipelines to collect, clean, and process large datasets from multiple sources, collaborate with product and engineering teams to integrate AI models into production systems, and implement MLOps practices such as model versioning, monitoring, CI/CD, and retraining pipelines. Additionally, you’ll conduct exploratory data analysis to guide model features, optimize models for accuracy and scalability, develop dashboards to communicate insights, and proactively propose innovative AI/ML solutions.Who They AreA fast-growing gaming company focused on delivering a high-quality, engaging experience for players. They combine data-driven insights with cutting‑edge AI to create smarter, more personalized gameplay. You’ll be part of a team that values innovation, collaboration, and the real‑world impact of AI on user engagement.What Is In It For YouWork on impactful, real‑world AI/ML projects in the gaming industryOpportunity to own end‑to‑end ML projects from concept to deploymentExposure to MLOps, cloud platforms, and scalable AI systemsProfessional growth and learning in advanced AI/ML techniques36k SAR + other benefitsRequirementsBachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field4–7 years of hands‑on experience in machine learning, data science, or AI engineeringProven experience delivering end‑to‑end ML projects from concept to deploymentStrong programming in Python (pandas, NumPy, scikit‑learn, TensorFlow or PyTorch)Solid understanding of ML algorithms (supervised, unsupervised, deep learning)Hands‑on experience with data engineering tools: ETL pipelines (Airflow, Prefect, or custom), SQL/NoSQL databases, cloud storage (S3, GCS)Experience with MLOps and production deployment: Docker, Kubernetes, APIs (FastAPI/Flask), CI/CD for ML (MLflow, Kubeflow, Vertex AI)Familiarity with cloud platforms (AWS, GCP, Azure ML)Proficiency in Git and collaborative workflows#J-18808-Ljbffr