About The Role

We are looking to hire passionate Senior AI Engineers to help turn data into intelligent, production-ready solutions. You will work across the full AI stack: traditional machine-learning models, large language models (LLMs), computer-vision pipelines, and analytics / forecasting workflows. If you enjoy exploring data, building state-of-the-art models, and shipping reliable AI services, we would love to meet you.

Responsibilities

Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion).
Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs.
End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring.
LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements.
AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines.
Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team.
Mentor junior engineers and contribute to technical direction and engineering best practices.

Requirements

BSc in Computer Science, Mathematics, or related field.
5+ years of professional experience working on AI/ML projects.
Good command of English and Arabic (written and spoken).
Proficient in Python and core libraries (PyTorch / TensorFlow, scikit-learn, pandas, NumPy).
Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics.
Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis.
Familiarity with at least one of: OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar.
Good grasp of software-engineering best practices: Git, code reviews, testing, CI.

Preferred Qualifications

Knowledge of C++ or C# for performance-critical modules.
Experience deploying models via Docker, Kubernetes, or cloud AI services.
Exposure to vector databases and RAG workflows.
Skill in BI / dashboard tools (Power BI, Tableau, Streamlit) or time-series frameworks (Prophet, statsmodels).
Familiarity with MLOps / LLMOps tooling (DVC, MLflow Tracking, Weights & Biases, BentoML).
Experience with image processing techniques (e.g., OpenCV, image segmentation, feature extraction)
Experience with Spark (PySpark) and distributed data processing, including usage of platforms such as Databricks, AWS EMR, or GCP Dataproc.
Strong SQL skills and experience working with large-scale datasets, including partitioning and performance tuning.
Familiarity with modern data lake architectures and scalable data storage concepts.