About Our ClientOur client is an established management consulting firm working with government, financial services, and enterprise clients across the GCC and wider MENA region. Their identity will be disclosed at first contact from Faze 3.The Team You'd Be JoiningA rapidly growing, ambitious AI engineering team being built from the ground up to design and ship the AI products at the centre of the client’s consulting work. As an early hire in this build, you will help shape both the technical foundations and the engineering culture of the function.The RoleThis 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.Mandatory RequirementsEducation. 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.Essential Skills:Strong PythonNumPypandasscikit-learnproduction ML pipelinesSQLworking 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• Time-series modelling depth — ARIMA family, Prophet, deep-learning sequence models.• PyTorch or TensorFlow at working level.• MLflow or equivalent experiment tracking in production use.• Embeddings, vector search, and reranking experience.• XGBoost, LightGBM, or CART model fluency.• Drift detection, shadow evaluation, or A/B test design experience.• A live GitHub or portfolio with original ML work.• Arabic language capability.How to ApplyYou will need your CV and a 300-word narrative describing one ML model you took from concept to production — problem, approach, evaluation, deployment, and measurable impact. A GitHub or portfolio link is welcomed but optional.