Job Description

Roles & Responsibilities

This role bridges classical machine learning and modern LLM-based systems. You will build production ML models (regression, classification, time-series, anomaly detection) alongside hybrid architectures combining retrieval-augmented generation, ML scoring, and rules-based logic. You will own the evaluation frameworks that keep applied AI honest in production: precision, recall, hypothesis testing, model diagnostics, drift detection.

This is a hands-on practitioner role, not research. The team ships into live client engagements against measured precision, recall, latency, and cost targets.

Essential Skills:

o Strong Python

o NumPy

o pandas

o scikit-learn

o production ML pipelines

o SQL

o working comfort with the LLM and RAG interplay.

Statistical rigour. Working fluency with hypothesis testing (t-tests, z-tests, chi-square), time-series and stationarity diagnostics (AD, JB, ARCH, ADF, KPSS), and model diagnostics (AIC, BIC, log-likelihood).

Conditional certification. If your degree is in Statistics (or very similar/related), the role requires either the Stanford Machine Learning Specialization OR the IBM Python for Data Science and AI certification. For CS-pathway candidates, these are good-to-have.

Languages. Proficiency in English is required.

Desirable Skills.

o Time-series modelling depth ARIMA family, Prophet, deep-learning sequence models.

o PyTorch or TensorFlow at working level.

o MLflow or equivalent experiment tracking in production use.

o Embeddings, vector search, and reranking experience.

o XGBoost, LightGBM, or CART model fluency.

o Drift detection, shadow evaluation, or A/B test design experience.

o A live GitHub or portfolio with original ML work.

o Arabic language capability.

Desired Candidate Profile

Education. Bachelor s in Computer Science (or very similar/related) OR Bachelor s in Statistics (or very similar/related) from a Tier 1 / Tier 2 university. Master s preferred. Very similar/related includes Software Engineering, Mathematics, Data Science, Physics, Applied Statistics, Information Systems, Econometrics, and similar substantively quantitative disciplines.

Experience. 5 10 years in applied ML / AI engineering, with at least 3 years building ML models that ran in production.