Machine Learning / AI Engineer 

Job Details

Category:

Information Technology

Career Level:

Experienced (Non-Managerial)

Experience:

3 Years

Required Qualification:

Bachelor's

Requires Traveling:

No

Salary:

Salary Not disclosed

Salary Type:

Per Month

Total Vacancies:

2

Skills

tuninglanguagesdatadata collectionmachine learningalgorithmsreinforcementperformancemetricsmatrixproductiondockercloudazuremonitordeploymentdesignmaintenanceproduct marketingengineeringtechnicalfunctionalresearchtoolsdeep learningcomputer visionsolutionsbuildingenvironmenttrackingmanagementeducationcomputer sciencedata sciencefieldpythoncloud computinggooglenosqllargeanalytical skillsdata analysiscommunicationstrong communication skillsconceptsspoken englisharabic

Description

Main Responsibilities

• • Collect and prepare data (Data Collection & Preprocessing) from various sources (databases, files, APIs).
  • Select and develop machine learning algorithms and models (Supervised, Unsupervised, Reinforcement).
  • Train and hyperparameter tune the model to ensure the highest accuracy and performance.
  • Evaluate the model using metrics such as Accuracy, AUC, MAE, and Confusion Matrix.
  • Deploy production models via Docker or cloud services (AWS SageMaker, Azure ML).
  • Monitor model performance after deployment and detect performance degradation (Model Drift).
  • Document model design and operational steps and create a maintenance runbook.
  • Collaborate with product, marketing, and engineering teams to understand technical and functional requirements.
  • Research and test new technologies and tools (Deep Learning, NLP, Computer Vision) to enhance solutions.
  • Contribute to building an integrated MLOps environment (ML CI/CD, experiment tracking, model management).
  
  
  Qualifications and Requirements
  
  
  Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  Experience: + years developing and deploying machine learning/AI models in production environments.
  
  
  Languages ​​and Tools:
  
  
  Proficiency in Python (libraries: TensorFlow, PyTorch, Scikit-Learn, Pandas, NumPy)
  Experience in Jupyter Notebooks, Git, Docker
  Familiarity with MLOps tools such as MLflow or Kubeflow
  Cloud Computing: Experience in AWS, Azure, or Google Cloud to deploy models and manage resources.
  Databases: Proficiency in SQL and NoSQL to handle large and diverse data.
  Analytical Skills: Excellent problem-solving, data analysis, and insights.
  Communication: Strong communication skills to simplify technical concepts for non-experts and clearly document work.
  Language: Fluency in written and spoken English, and knowledge of technical Arabic terms is preferred.