Senior Machine Learning Engineer

Key Responsibilities:

1. Arabic Speaker: A must.
2. Team Leadership: Mentor and guide junior to mid-level ML engineers and developers, fostering a collaborative and innovative environment.
3. Model Development: Build, train, and optimize robust AI models using state-of-the-art techniques in NLP, speech analysis, and recognition.
4. AI Service Packaging: Package AI models as services, ensuring they are ready for deployment in production environments.
5. Deployment: Deploy AI services using microservices architecture and Docker containers for scalable and reliable operation.
6. Automated Pipelines: Design and implement automated machine learning pipelines for model training, testing, and inference.
7. Scalable Deployments: Develop and manage scalable deployments and distributed training processes to handle large-scale data and models.
8. Performance Monitoring: Continuously monitor and evaluate model performance, making necessary adjustments to improve accuracy and efficiency.
9. Full-Stack Development: Utilize strong full-stack development skills to contribute to the software development lifecycle, including front-end and back-end development.
10. Software Engineering: Apply software engineering best practices in the development, testing, and maintenance of AI systems.
11. Collaboration: Work closely with cross-functional teams, including data scientists, product managers, and software engineers, to deliver high-quality AI solutions.

Required Skills and Qualifications:

1. Education: Master's degree in Artificial Intelligence, Computer Science, or a relevant field. A Ph.D. is a plus.
2. 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).
   • Hands-on experience with transformers AI models, language models, and fine-tuning.
3. 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.
   • 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.
4. LLM Skills:
   • In-depth understanding of Large Language Models (LLMs) and their architectures, including transformer-based models like GPT and BERT.
   • 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 tasks and context understanding.
   • Knowledge of techniques for optimizing LLMs for performance, including parameter tuning, regularization, and model compression.