Data Engineer

Role overview

Key responsibilities

• Design, build, and maintain data pipelines, ETL workflows, and databases for AI/ML projects.
• Develop and deploy LLMs and NLP pipelines for real-world applications.
• Preprocess and clean data, extract features, and train models on structured and unstructured data.
• Fine-tune and optimize foundation models such as GPT, LLaMA, and Falcon using domain-specific datasets.
• Collaborate on model evaluation, interpretability, and explainability.
• Integrate AI models with front-end applications, APIs, and databases for operational deployment.
• Support research publications, grant proposals, and technical documentation.

Minimum qualifications

• Bachelor’s degree in Computer Science, AI, Data Science, Data Engineering, or a related field.
• Strong Python programming skills and experience with data-processing libraries such as Pandas, NumPy, or PySpark.
• Machine-learning model development experience with TensorFlow, PyTorch, or Scikit-Learn.
• Solid understanding of data structures, algorithms, and statistical methods.
• Experience with SQL/NoSQL databases and AWS, Azure, or GCP cloud platforms.

Preferred qualifications

• A master’s degree in AI, Data Science, or a related field is preferred.
• LLM fine-tuning, distillation, or on-premises deployment experience is preferred.
• Knowledge graph or RAG pipeline experience; distributed computing with Spark, Dask, or Ray; and data lake architecture knowledge are preferred.
• Production-grade web or enterprise AI integration experience, open-source AI contributions, or high-impact AI/ML publications are preferred.
• Arabic NLP and bilingual EN/AR model development are a plus.

Expected skills

• NLP and LLM concepts, including tokenization, embeddings, transformers, and model fine-tuning.
• Familiarity with LangChain, Hugging Face Transformers, and OpenAI, Anthropic, or Cohere APIs.
• Understanding of MLOps, Docker, and deployment tools such as FastAPI, Streamlit, or MLflow.
• Knowledge of data versioning and reproducibility tools such as DVC and Git.
• Ability to handle large-scale multimodal text, audio, video, and sensor datasets.
• Analytical problem-solving, research mindset, and written and verbal communication for interdisciplinary collaboration.

Application deadline

• The stated closing date is 31 December 2026.