Responsibilities:

Design, develop, and optimize cutting-edge AI models with a focus on Generative and Agentic AI applications.
Collaborate closely with cross-functional teams to integrate AI solutions into existing products and services.
Conduct research to advance the state of the art in AI, contributing to publications in top-tier journals and conferences.
Implement robust machine learning algorithms that enhance the performance and capabilities of AI systems.
Lead the development of scalable AI systems, ensuring high efficiency and accuracy.
Evaluate and improve existing AI models to align with the latest technological advancements.
Mentor junior engineers and drive initiatives for professional development within the AI team.

Requirements:

Over 10 years of experience in AI development, with specific expertise in Generative and Agentic AI.
Proven track record of developing large-scale AI systems in a commercial setting.
Strong programming skills in Python, TensorFlow, PyTorch, or similar AI frameworks.
Deep understanding of machine learning principles, neural network architecture, and natural language processing.
Excellent problem-solving skills and ability to innovate using latest AI technologies.

Qualifications:

Master’s or Doctoral degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.
Extensive publication history in reputable AI conferences and journals is desirable.
Experience with AI model deployment in real-world applications.
Strong communication skills and ability to articulate complex technical concepts to non-technical stakeholders.
Proven leadership experience in managing large and diverse engineering teams.

Additional Skills:

Experience with cloud-based AI solutions and architectures.
Familiarity with data privacy and ethical considerations specific to AI solutions.
Ability to work on-site full-time and lead collaborative projects effectively.
Familiar with agile development methodologies and version control systems.

المهارات المطلوبة

Generative AIAgentic AINeural network architecture and machine learning principlesNatural Language Processing (NLP)Python programmingTensorFlow and PyTorch (deep learning frameworks)Developing large-scale, scalable AI systemsAI model deployment and cloud-based AI architecturesResearch and publication in top-tier AI conferences and journalsLeadership, mentoring, and managing large engineering teamsCross-functional collaboration and communicating technical concepts to non-technical stakeholdersData privacy, ethics, and responsible AI considerationsAgile development methodologies and version control systems

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