Quant AI Engineer

Welcome to MultiBank Group, a global financial pioneer established in 2005 in California and now proudly headquartered in Dubai, UAE. We specialize in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.

Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. As a heavily regulated institution with oversight from 18+ financial regulators across 5 continents, and recipient of over 80 financial awards, MultiBank Group is devoted to innovation, excellence, and empowering our clients to achieve their financial goals.

Role Overview

We are seeking a Quant AI Engineer to design, develop, and deploy AI-powered quantitative systems that support trading, risk management, market surveillance, forecasting, and operational intelligence.

The successful candidate will combine quantitative development, machine learning engineering, and software engineering expertise to build scalable production-grade AI solutions. This role is focused on transforming quantitative models and AI research into reliable, high-performance systems capable of operating in real-time financial environments.

Key Responsibilities

AI & Quantitative Model Development

• Develop machine learning and quantitative models for forecasting, risk analytics, pricing, anomaly detection, and market intelligence.
• Design and implement predictive models using statistical, machine learning, and deep learning techniques.
• Optimize model accuracy, reliability, and performance across production environments.
• Build frameworks for model evaluation, experimentation, and continuous improvement.

AI Platform Engineering

• Build scalable machine learning pipelines for model training, deployment, monitoring, and lifecycle management.
• Develop APIs, services, and infrastructure that expose AI and quantitative capabilities to business applications.
• Implement MLOps practices including model versioning, monitoring, governance, and automated deployment.
• Ensure high availability, scalability, and observability of AI systems.

LLM & Agentic AI Solutions

• Design and implement LLM-powered applications for research automation, knowledge extraction, decision support, and operational workflows.
• Develop Retrieval-Augmented Generation (RAG) architectures using internal and external datasets.
• Build AI agents and workflow orchestration solutions that automate complex business processes.
• Evaluate emerging AI technologies and integrate them into production systems where appropriate.

Data & Systems Integration

• Integrate AI solutions with trading platforms, market data feeds, risk systems, and enterprise applications.
• Work with structured and unstructured datasets across real-time and batch processing environments.
• Ensure data quality, security, governance, and regulatory compliance across AI initiatives.

Technical Requirements

• Strong software engineering experience using Python.
• Experience with machine learning frameworks including PyTorch, TensorFlow, Scikit-learn, and XGBoost.
• Strong understanding of machine learning engineering, model deployment, and MLOps practices.
• Experience building production-grade AI and machine learning systems.
• Knowledge of LLM frameworks such as LangChain, LangGraph, LlamaIndex, OpenAI APIs, or AWS Bedrock.
• Experience building RAG systems, vector database solutions, and AI agents.
• Experience with SQL, Spark, Kafka, Docker, Kubernetes, and CI/CD pipelines.
• Familiarity with AWS, Azure, or GCP cloud platforms.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Sc