Senior Quantitative ML Engineer — Production Models

Boundless; Dubai; On-site; AED 350,000 - 600,000; Full time

Job summary

Boundless is seeking a seasoned quantitative researcher to develop, validate and productionise ML models for understanding trading behaviour, execution quality and market exposure. You will turn research into reliable model services and collaborate with Dealing and Risk to ensure responsible use.

You will work with Python, SQL and time-series methods, handle real trading data, and contribute to model explainability, robustness and monitoring in a fast-paced brokerage environment.

Qualifications

• Strong Python, SQL, probability, statistics, optimisation and time-series analysis skills.
• Hands-on experience with scikit-learn, XGBoost, LightGBM or PyTorch.
• Experience with trading/execution data and risk analytics.
• Degree in a quantitative field; postgraduate study advantageous.

Responsibilities

• Translate approved research hypotheses into features, labels and models for behavioural classification, anomaly detection, exposure forecasting and execution-quality analysis.
• Develop models using trading activity, price movements, order timing, holding periods, execution data and account history, with clear definitions of what each prediction represents.
• Investigate concentration, correlated activity, changes in trading patterns and adverse‑selection indicators using supervised, unsupervised or time-series techniques as appropriate.
• Benchmark machine learning against transparent statistical and rules-based baselines; establish whether added complexity produces meaningful incremental value.
• Produce explainability, calibration, sensitivity and stability analyses; document assumptions, training populations, limitations and conditions under which the model should not be used.
• Package models for batch or online inference with versioned code, features, datasets and dependencies, working with MLOps on deployment and rollback.
• Implement model monitoring for data drift, prediction changes, performance deterioration and operational failures; distinguish changing markets from broken data.
• Run shadow or controlled challenger assessments before operational use, with approval and independent challenge for material trading or risk applications.
• Investigate model failures and refresh models through reviewed change procedures; support the Quantitative Lead in explaining results to Dealing and Risk.

Skills

Python

SQL

Time-series

Statistics

Probability

ML frameworks

Model deployment

Experiment tracking

Education

Bachelor's in Mathematics/Statistics/CS/Engineering

Postgraduate study advantageous

Tools

scikit-learn

XGBoost

LightGBM

PyTorch

Job description

Boundless is seeking a seasoned quantitative researcher to develop, validate and productionise ML models for understanding trading behaviour, execution quality and market exposure. You will turn research into reliable model services and collaborate with Dealing and Risk to ensure responsible use.

You will work with Python, SQL and time-series methods, handle real trading data, and contribute to model explainability, robustness and monitoring in a fast-paced brokerage environment.