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.

Machine Learning Engineer