Key Responsibilities:

• Team Leadership: Mentor and guide junior to mid-level ML engineers and developers, fostering a collaborative and innovative environment.
• Model Development: Build, 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.
• Full-Stack Development: Utilise strong full-stack development skills to contribute to the software development lifecycle, including front-end and back-end development.
• Software Engineering: Apply software engineering best practices in the development, testing, and maintenance of AI systems.
• Collaboration: Work closely with cross-functional teams, including data scientists, product managers, and software engineers, to deliver high-quality AI solutions.

Skills and Qualifications

Education: Degree in Artificial Intelligence, Computer Science, or a relevant field.

Experience:

• Proven experience in machine learning engineering with a focus on building and deploying AI models.
• Extensive hands-on experience with ML frameworks and tools (TensorFlow, PyTorch, etc.).
• Hands-on experience with transformers AI models, language models, and fine-tuning/training large language models (LLMs).

Technical Skills:

• Deep understanding of ML, DL, Generative AI models, NLP, and speech analysis and recognition.
• Proficiency in Python and Java programming, with strong coding skills and experience in developing large-scale AI systems.
• Experience with microservices architecture and Docker containers for developing and deploying scalable AI services.
• Expertise in designing and implementing automated ML pipelines for efficient model training, testing, and inference.
• Knowledge of scalable deployments and distributed training techniques, leveraging cloud platforms and distributed computing resources.
• Strong full-stack development skills, including proficiency in front-end and back-end technologies.
• Familiarity with Ubuntu server commands and basic DevOps skills, ensuring robust and reliable AI infrastructure.
• Experience working with NLP AI models processing Arabic language models.

LLM Skills:

• In-depth understanding of Large Language Models (LLMs) and their architectures, including transformer-based models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers).
• Experience in fine-tuning and training large language models on specific tasks or domains, such as text generation, language translation, or sentiment analysis.
• Proficiency in working with pre-trained LLMs and adapting them to custom applications, including handling tasks like tokenization, attention mechanisms, and context understanding.
• Knowledge of techniques for optimizing LLMs for performance, including parameter tuning, regularisation, and model compression.

Language Proficiency: Proficiency in Arabic language is a plus.