Position Objective
Support Tahakom’s data science and machine learning initiatives by developing, deploying and maintaining reliable AI models and Machine Learning Operations (MLOps) pipelines. Ensure models are reproducible, scalable, auditable and aligned with business requirements and enterprise governance standards.
Job Description & Responsibilities
- Design, develop and evaluate machine learning and deep learning models for prediction and forecasting.
- Prepare datasets, fine-tune computer vision models and conduct structured evaluations for domain-specific applications.
- Develop and maintain reproducible training and deployment pipelines across development, testing and production environments.
- Maintain experiment tracking, model versioning and dataset lineage to support reproducibility and auditability.
- Deploy models as reliable production services and monitor model performance and data drift.
- Retrain or roll back models based on performance evidence and operational requirements.
- Document model design, assumptions, evaluation results and limitations for governance and technical review.
- Collaborate with engineers, project leads and business stakeholders to deliver solutions and communicate results clearly to technical and non-technical audiences.
Qualifications & Experience
- Bachelor’s or Master’s degree in Computer Science, Engineering or a related quantitative discipline.
- Minimum 4 years of hands-on machine learning engineering experience, including deploying models to production.
- Strong Python programming skills and proficiency in PyTorch or TensorFlow, and scikit-learn.
- Demonstrated experience fine-tuning and evaluating computer vision models using custom datasets.
- Experience in predictive modelling using tabular and time-series data, with appropriate validation methods.
- Practical experience with experiment tracking, model registries such as MLflow, and data and model versioning.
- Experience with Docker and continuous integration and delivery (CI/CD) pipelines for machine learning.
- Experience working with Linux and cloud or GPU-based training environments.
- Strong written and spoken English, with effective technical documentation and stakeholder communication skills.
- Experience optimising models for edge or on-device inference, including quantisation and ONNX export.
- Experience with model-serving frameworks or Kubernetes-based deployment.
- Experience delivering AI solutions in enterprise or regulated environments.
- Familiarity with generative AI and retrieval-based systems.
- Arabic language proficiency.
