In this role, you will be responsible for designing, building, and deploying machine learning models to solve complex business challenges. You’ll collaborate closely with data scientists, software engineers, and product stakeholders to ensure our AI solutions are robust, scalable, and deliver measurable impact. This is an exciting opportunity for someone who is passionate about state-of-the-art ML techniques and wants to play a key role in driving innovation.

Responsibilities & Technical Operational Duties                                                        

1. Model Development & Deployment

• Design, train, and fine-tune ML models to address specific product or business needs.
• Implement best practices for model evaluation, including error analysis and iterative improvements.
• Deploy models into production environments, ensuring scalability, performance, and reliability.

2. Data Pipeline & Architecture

• Collaborate with Data Engineering teams to design and optimize data pipelines, ensuring high-quality, clean, and well-labeled datasets.
• Develop automated workflows for feature engineering, model training, and inference using MLOps tools and frameworks.

3. Infrastructure & MLOps

• Implement CI/CD pipelines for ML, managing model versioning, monitoring, and automated rollbacks when necessary.
• Set up and maintain ML environments in cloud or on-premise platforms (AWS, Azure, GCP, etc.)

4. Collaboration & Cross-Functional Support

• Work closely with Data Scientists to translate research and prototypes into production-grade solutions.
• Partner with Software Engineers to integrate ML models into existing applications and services.
• Provide technical guidance and mentorship to junior engineers, promoting a culture of continuous learning and improvement.
• Monitoring & Performance Optimization
• Develop and use monitoring tools to track model performance in real time, proactively identifying and addressing drift or performance degradation
• Continuously optimize model speed and accuracy, balancing trade-offs between computational complexity and production constraints.

General Administrative Duties

• Active team player, self-starter, and multitasker who can quickly adjust priorities

Job Specifications

Experience

3–5/5+ – Customize based on level] years of experience in machine learning, data science, or software engineering with a focus on ML.

Languages Required

• Proficiency in both written and spoken Arabic and English languages

Required Skills and certificates

– Bachelor’s or master’s degree in computer science, Engineering, or a related field (Ph.D. is a plus).

– Programming Languages: Strong proficiency in Python; familiarity with Scala or R is a plus.

– ML Frameworks: Hands-on experience with TensorFlow, PyTorch, or Scikit-Learn.

– Data & Databases: Proficiency in SQL; exposure to NoSQL databases (MongoDB, Cassandra) is beneficial.

– Cloud & DevOps: Experience with AWS, GCP, or Azure services for ML workloads, and containerization (Docker, Kubernetes).

– MLOps & CI/CD: Familiarity with MLflow, Kubeflow, Airflow, or similar tools for automating the ML lifecycle.

– APIs & Integration: Ability to create, maintain, and consume RESTful APIs or gRPC services for model deployment.

– Collaboration: Effective communication skills for working with cross-functional teams.

– Problem-Solving: Strong analytical and critical-thinking abilities, able to break down complex problems.

– Agility: Comfortable in a fast-paced, iterative environment, adjusting quickly to project needs.

– Leadership & Mentorship: Willingness to guide less-experienced team members and contribute to best practices.