Applied AI Engineer

Remal Ventures; Saudi Arabia; On-site; SAR 224,971 - 299,962; Full time

Job summary

A cutting-edge technology company in Saudi Arabia is seeking a highly skilled Applied AI/Machine Learning Engineer to build end-to-end machine learning solutions. The ideal candidate has a strong background in Python programming, machine learning algorithms, and data engineering. You will design models to enhance player experience and collaborate with cross-functional teams in a dynamic environment. This role offers opportunities for innovation and growth.

Qualifications

• 3–7 years of hands-on experience in machine learning, data science, or AI engineering.
• Proven experience delivering end-to-end ML projects from concept to deployment.

Responsibilities

• Design, develop, and deploy machine learning models for real-world use cases.
• Build and maintain data pipelines (ETL/ELT).
• Collaborate with product and engineering teams to integrate AI models into production.
• Implement and manage MLOps practices.
• Conduct exploratory data analysis to guide model features.
• Optimize models for accuracy, latency, and scalability.
• Develop dashboards and reports to communicate model performance.
• Stay current with advances in ML/AI frameworks.

Skills

Programming in Python

Machine learning algorithms

Data engineering tools

MLOps / production deployment

Cloud platforms (AWS, GCP, or Azure ML)

Version control (Git)

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field

Tools

TensorFlow

PyTorch

SQL

NoSQL databases

Docker

Kubernetes

Job description

We’re looking for a highly skilled Applied AI/Machine Learning Engineer who can bridge the worlds of data science, data engineering, and AI deployment. You’ll be responsible for building end‑to‑end machine learning solutions — from collecting and preparing data, to training, evaluating, and deploying models that enhance the Kammelna Games player experience (e.g., churn prediction, personalization, user engagement optimization).This role is ideal for someone who thrives at the intersection of hands‑on data work, machine learning model development, and scalable AI systems.

Responsibilities

• Design, develop, and deploy machine learning models for real‑world use cases (player behavior prediction, personalization, retention modeling, fraud detection, etc.).
• Build and maintain data pipelines (ETL/ELT) to collect, clean, and process large datasets from multiple sources (game analytics, APIs, user data).
• Collaborate with product and engineering teams to integrate AI models into production environments (APIs, backend systems).
• Implement and manage MLOps practices — model versioning, CI/CD for ML, monitoring, and retraining pipelines.
• Conduct exploratory data analysis (EDA) to uncover insights and guide model features.
• Optimize models for accuracy, latency, and scalability.
• Develop dashboards and reports to communicate model performance and insights to non‑technical stakeholders.
• Stay current with advances in ML/AI frameworks (LLMs, deep learning, reinforcement learning) and proactively propose innovative ideas.

Core Technical Skills

• Strong programming in Python (pandas, NumPy, scikit‑learn, TensorFlow or PyTorch).
• Solid understanding of machine learning algorithms (supervised, unsupervised, deep learning).
• Hands‑on experience with data engineering tools:
  • ETL frameworks (Airflow, Prefect, or custom pipelines)
  • SQL and NoSQL databases (PostgreSQL, BigQuery, MongoDB)
  • Data storage (S3, GCS, etc.)
• Experience with MLOps / production deployment:
  • Containerization (Docker, Kubernetes)
  • APIs (FastAPI, Flask)
  • CI/CD for ML (MLflow, Kubeflow, or Vertex AI)
• Familiarity with cloud platforms (AWS, GCP, or Azure ML).
• Proficiency in version control (Git) and collaborative workflows.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 3–7 years of hands‑on experience in machine learning, data science, or AI engineering.
• Proven experience delivering end‑to‑end ML projects from concept to deployment.