AI Engineer

Position Overview:

We seek experienced AI Engineers with a robust background in machine learning and LLMs to join our team. The ideal candidates should possess expertise in the following areas:

Key Responsibilities:

Implement and manage LLMs for image processing, including handwriting recognition.

Setup and deploy LLMs on AWS Cloud (UAE Region) and local servers.

Integrate local APIs with external and internal systems.

Fine-tune and train LLM models, ensuring high-quality output.

Troubleshoot and resolve issues during model training and deployment.

Design and deploy high-speed network technologies for AI/LLM applications.

Develop platforms for monitoring, analysis, and diagnosis of large-scale AI/LLM networks.

Research and develop high-performance AI communication frameworks and network protocols.

Build next-generation AI network infrastructure to support large-scale heterogeneous network hardware.

Design and develop AI models using OpenAI technologies.

Integrate AI models into existing systems and applications.

Create and manage local LLMs, storing data in local databases.

Qualifications:

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or related field.

Deep understanding of AI algorithms, data structures, and software development principles.

Knowledge of Langchain, OpenAI, and open-source AI frameworks.

Proven experience in developing and working with large language models.

Strong foundation in machine learning concepts and techniques, including deep learning architectures and NLP.

Proficiency in programming languages such as Python, .Net, TensorFlow, PyTorch and NodeJS.

Expertise in pre-processing and cleaning large datasets for training models.

Familiarity with data augmentation techniques and model evaluation metrics.

Experience in deploying language models in production environments.

Strong teamwork and communication skills to collaborate with cross-functional teams.

A proactive approach to identifying and solving challenges in language model development.

A portfolio showcasing previous language model projects is a plus.