Applied ML and AI Engineer

About Our Client

Our 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 Joining

A 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 Role

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.

Mandatory Requirements

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.

Core technical.

• Strong Python
• NumPy, pandas, scikit-learn
• Production ML pipelines
• SQL
• 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.

Language: English proficiency 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.

Salary is highly competitive and dependent on your skills and experience.

How to Apply

Apply through this platform and we will contact you to direct you to our online application portal.

Job Types: Full-time, Permanent

Pay: E£25,000.00 - E£135,000.00 per month

Work Location: In person