Machine Learning Engineer (UAE National)

Job Purpose

Job Responsibilities

• R&D Experimentation: Conduct cutting-edge research on ML, DL, Generative AI models, NLP, and speech recognition technologies to drive innovation and improve our AI solutions.
• Model Development: crawl data from various datasources, develop pipelines, build models, train, and optimize robust AI models using state-of-the-art techniques in NLP, speech analysis, and recognition.
• AI Service Packaging: Package AI models as services, ensuring they are ready for deployment in production environments.
• Deployment: Deploy AI services using microservices architecture and Docker containers for scalable and reliable operation.
• Automated Pipelines: Design and implement automated machine learning pipelines for model training, testing, and inference.
• Scalable Deployments: Develop and manage scalable deployments and distributed training processes to handle large-scale data and models.
• Performance Monitoring: Continuously monitor and evaluate model performance, making necessary adjustments to improve accuracy and efficiency.
• Collaboration: Work closely with cross-functional teams, including data scientists, product managers, and software engineers, to deliver high-quality AI solutions.

Technical Skills

• Deep understanding of ML, DL, Generative AI models, NLP, and speech analysis and recognition.
• Proficiency in Python and Java programming and strong coding skills.
• Experience with microservices architecture and Docker containers.
• Expertise in automated ML pipelines for training, testing, and inference.
• Knowledge of scalable deployments and distributed training techniques.
• Familiarity with Ubuntu server commands and basic DevOps skills.

Preferred Educational Qualifications and Professional Certifications

• Bachelor’s degree in Artificial Intelligence, Computer Science, or a relevant field.

Experience

• UAE National fresh graduates are encouraged to apply.
• 1–2 years of experience in AI/ML is required.
• Proven experience in machine learning engineering, with a focus on R&D and production-level deployment.
• Extensive hands-on experience with ML frameworks and tools such as TensorFlow, PyTorch, etc.