Quant AI Engineer - AI Engineering, LLM Platforms & Quantitative Trading Intelligence

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 OverviewWe are looking for a Quant AI Engineer to lead the adoption of Generative AI and Large Language Models (LLMs) across MultiBank Group’s quantitative trading ecosystem. This is not a traditional quantitative developer role. We are looking for an LLM Engineer with strong software engineering experience who can build AI-powered platforms that accelerate trading operations, research, risk management, compliance, treasury, and brokerage workflows. You will design, build, and deploy enterprise-grade AI agents, LLM applications, and intelligent automation solutions that integrate with our trading platforms, market data, CRM, risk systems, reconciliation engines, and operational infrastructure. Working closely with traders, quantitative researchers, risk managers, legal, compliance, treasury, and technology teams, you will identify high-value AI use cases and deliver production-ready AI solutions that improve productivity, decision-making, and operational efficiency. Key ResponsibilitiesLLM Engineering for Quantitative Trading Design, build, and deploy AI assistants and LLM-powered applications that support traders, quantitative researchers, dealing desks, and operations teams. Build Retrieval-Augmented Generation (RAG) platforms capable of answering complex questions using internal research, market data, trading documentation, and operational knowledge. Develop AI copilots that assist in quantitative research, strategy development, trading analytics, and market intelligence. Integrate frontier LLMs including Claude, OpenAI, Gemini, and open-source models into production trading environments. Evaluate, benchmark, and optimise LLM performance for financial use cases, ensuring accuracy, latency, and reliability. AI Agents for Brokerage Operations Design autonomous AI agents that automate workflows across onboarding, CRM, customer operations, treasury, compliance, finance, and trading support. Build multi-agent systems capable of orchestrating complex operational processes using frameworks such as LangGraph, CrewAI, MCP, and similar technologies. Develop AI assistants for internal teams capable of executing tasks, retrieving information, generating reports, and interacting with enterprise systems. Integrate AI agents with trading platforms, CRMs, databases, document repositories, and internal APIs. AI-Powered Risk & Trading Intelligence Build AI solutions that analyse trading behaviour, identify portfolio risks, detect anomalies, and surface actionable insights for risk managers. Develop intelligent dashboards and copilots that explain risk exposure, margin utilisation, trading performance, and market events using natural language. Implement AI-powered monitoring for trading operations, execution quality, and operational incidents. Build intelligent alerting systems capable of recommending corrective actions based on live market conditions and trading data. Introducing Broker (IB) Intelligence Develop AI-powered solutions supporting the complete Introducing Broker (IB) lifecycle, including onboarding, document verification, commission management, and performance analytics. Build LLM-powered assistants that answer operational, financial, and contractual questions relating to IB relationships. Develop AI models that identify high-performing brokers, detect fraud, and recommend retention opportunities. Automate reporting, commission validation, and operational workflows using AI agents. AI Reconciliation & Financial Operations Build AI-assisted reconciliation platforms capable of comparing transactions across trading platforms, payment gateways, banks, CRM systems, liquidity providers, and finance systems. Develop intelligent exception management agents that identify mismatches, classify reconciliation breaks, recommend root causes, and automate investigation workflows. Implement AI-powered operational dashboards providing reconciliation insights, financial controls, and exception analytics. Build AI copilots that assist finance and operations teams in resolving reconciliation issues faster. Agreement Intelligence & Legal AI Build LLM-powered document intelligence solutions capable of parsing and understanding Client Agreements, IB Agreements, Liquidity Provider Agreements, Prime Broker Agreements, ISDA documentation, and regulatory documents. Develop AI systems that extract key clauses, identify risks, compare agreement versions, summarise legal documents, and highlight compliance obligations. Build semantic search capabilities enabling business users to query legal and operational documentation using natural language. Automate document review and approval workflows using AI agents. Enterprise LLM Platform Engineering Design enterprise-grade LLM infrastructure supporting prompt management, model orchestration, vector databases, embeddings, RAG pipelines, evaluation frameworks, guardrails, and observability. Develop scalable AI APIs and reusable AI services that can be consumed across multiple business functions. Build monitoring frameworks measuring hallucination rates, response quality, latency, usage, and model performance. Implement secure AI governance, access control, audit logging, and responsible AI practices across production systems. AI Engineering Excellence Establish best practices for prompt engineering, evaluation, testing, model selection, and AI application architecture. Mentor engineering teams on effective LLM adoption, AI development patterns, and modern AI engineering practices. Build reusable AI frameworks that accelerate enterprise AI delivery across the organisation. Continuously evaluate emerging LLM technologies and recommend adoption strategies that improve MultiBank’s AI maturity. Qualifications5–10 years of software engineering experience with strong Python development skills. Hands-on experience building production LLM applications using Claude, OpenAI, Gemini, or open-source models. Experience developing AI agents, RAG systems, prompt engineering, vector databases, and agent orchestration frameworks. Strong understanding of APIs, distributed systems, cloud platforms, and enterprise software integration. Experience integrating AI solutions with financial systems, trading platforms, CRMs, or operational platforms is highly desirable. Familiarity with brokerage operations, capital markets, risk management, or quantitative trading is an advantage. Experience deploying AI applications into production with monitoring, evaluation, and governance. Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related discipline. Why Join Us?Work with an industry-leading global financial institution.Competitive salary and comprehensive employee benefits.Opportunities for professional growth and career advancement.Collaborative, inclusive, and dynamic work environment.Commitment to innovation and professional excellence.Become part of our international community at MultiBank Group, dedicated to excellence, innovation, and shaping the future of finance.