ABOUT THE ROLE

You will own end-to-end delivery of trading systems — from design and backtest through to live deployment — working across our brokerage's full stack of algorithmic trading, risk management, and market infrastructure. Alongside experienced quants and treasury, you will operate on real capital in live markets across multiple asset classes.

This role suits a self-starter with 3–5 years of production experience who is comfortable taking full ownership of projects, making architectural decisions, and iterating quickly in a fast-moving trading environment.

WHAT YOU WILL BE DOING

• Design and implement live trading systems covering flow analysis, hedging algorithms, position management, and real-time risk controls

• Own the full development lifecycle of quantitative infrastructure: strategy research, backtesting, simulation, and production deployment

• Develop pricing models, hedging systems, and portfolio optimization algorithms grounded in mathematical finance

• Build and maintain real-time market data pipelines and trading API integrations across exchanges, brokers, and liquidity providers

• Create production monitoring and analytics dashboards to track trading performance, system health, and market conditions

• Apply ML and AI agent techniques — signal generation, anomaly detection, pattern recognition, autonomous strategy workflows — where they create measurable edge

• Drive reliability and scalability improvements across existing trading infrastructure

WHAT YOU NEED TO BE SUCCESSFUL

Core requirements:

• Bachelor's or Master's degree in Computer Science, Financial Engineering, Mathematics, Physics, Statistics, or a related quantitative field

• 3–5 years of professional experience owning quantitative systems in production — backtesting frameworks, execution algorithms, or risk management systems — at a brokerage, fintech, or trading firm

• Expert-level Python with deep command of numpy, pandas, scipy, asyncio and numba; proven track record of performance-tuning production-grade numerical code

• Solid grasp of financial markets: trading mechanics, order types, market microstructure, and exchange API integration (e.g. Binance, OKX, Bybit, or similar)

• Hands-on experience with real-time data streaming: WebSocket, message queues (Kafka or equivalent), and low-latency system design

• Strong mathematical foundation with ability to implement quantitative models and statistical methods from research papers

• Working knowledge of P&L calculation, position sizing, portfolio risk metrics, and core derivatives pricing concepts

• Comfortable with agentic coding workflows (e.g. Claude Code, Cursor, or similar) and experienced in reviewing, validating, and shipping AI-generated code into production — including testing, code review discipline, and knowing when not to trust the model

• Self-motivated with ability to take full ownership from design through production deployment with minimal direction

Bonus if you also have:

• Understanding of derivatives products (options, futures, perpetuals) and margin trading mechanics — margin requirements, liquidation risk, funding rates

• Familiarity with ML frameworks (scikit-learn, XGBoost, or similar) and their application to financial prediction problems; experience building or orchestrating AI agents in a production context is a strong plus

• Experience with brokerage operations, cryptocurrency trading systems, arbitrage strategies, or cross-exchange workflows

• Knowledge of market-making, liquidity provision, or systematic trading strategies in live environments

• Contributions to quantitative finance open-source projects or published research
  
  Take the next step - submit your application here:
  https://pluanghrms.darwinbox.com/ms/candidatev2/main/careers/jobDetails/a69df2139b5e67?from=all

WHAT SUCCESS LOOKS LIKE

In your first 90 days, you will own at least one live system improvement deployed to production — whether a latency reduction, a new hedging signal, an improved risk control, or a data pipeline upgrade. Within six months, you will be the primary technical owner of one or more core trading systems, with full visibility into their performance and a roadmap for future development.

Position Description

As a AI Engineer (Agents & Data Intelligence), you will build AI-powered systems that transform how business teams access, analyse, and act on data. You will develop intelligent agents that automate routine analysis, generate insights, and dramatically reduce the time from question to answer—from days to minutes.

You will work across the AI layer of our data stack, building and extending our analysis chatbot (currently SaaS-powered, transitioning to in-house), developing specialised agents for planning, querying, analysis, and monitoring, and enabling the data governance infrastructure needed for company-wide AI-driven analytics. You will collaborate closely with data analysts, data engineers, and business stakeholders.

What You Will Be Doing

AI Agent Development

• Design and implement AI agent workflows powered by LLMs for end-to-end data tasks: natural language queries, automated analysis, reconciliation, and reporting

• Extend and improve our analysis chatbot by developing specialised agents for planning,

  querying, data quality validation, and monitoring

• Extend and optimise our existing RAG framework, including improving document processing,

  chunking strategies, embedding pipelines, and retrieval mechanisms to ground agent responses in our data context

• Develop prompt engineering strategies, few-shot learning, and context optimisation to improve agent

  accuracy and reliability

• Build orchestration frameworks that coordinate multiple specialised agents to solve complex data problems

Data Infrastructure & Governance for AI

• Work with data engineers and managers to build AI-friendly data pipelines that enable rapid AI adoption across the company

• Support implementation of the data access management architecture to enable granular, role-based access control for AI tools

• Build and maintain metric definition frameworks and documentation required for AI agents to accurately interpret business questions

• Develop automated data reconciliation systems including cross-system matching, discrepancy detection, and exception flagging

Automation & Business Impact

• Productionise AI use cases that replace manual processes, such as automated reporting, marketing analytics,

  customer segmentation, or anomaly detection

• Apply traditional ML techniques (anomaly detection, pattern recognition, classification) within agent workflows to

  enhance monitoring and decision-making

• Build quality assurance pipelines that evaluate LLM outputs for accuracy, consistency, and hallucination detection

• Collaborate with data analysts and business teams to identify automation opportunities and eliminate repetitive manual analysis

What You Need to Be Successful

Required

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field

• 3+ years of professional experience in machine learning engineering, data engineering, or

  software engineering with demonstrable ML project delivery

• Strong Python programming skills with hands-on experience using scientific computing libraries and data/visualisation tools

• Working experience with LLM APIs (e.g. OpenAI, Anthropic, or open-source models) and

  practical prompt engineering in production or project settings

• Familiarity with RAG architectures, vector databases, or document retrieval systems

• Solid SQL skills and experience working with cloud data warehouses

• Understanding of software engineering best practices: Git, CI/CD, API design, testing, and containerisation

• Strong analytical ability to translate business problems into technical implementations

• Good communication skills with both technical and non-technical stakeholders

• We welcome all applicants who are eligible to work in Singapore
  
  Take the next step — submit your application here: https://pluanghrms.darwinbox.com/ms/candidatev2/main/careers/jobDetails/a69c4eb1b5a57d?from=all

Nice to Have

• Experience with agent-based AI systems and orchestration frameworks (e.g. LangChain, LangGraph, CrewAI, or similar)

• Familiarity with GCP services (Cloud Run, Cloud Functions, BigQuery)

• Experience with dbt or data transformation frameworks

• Exposure to product analytics tools (e.g. Mixpanel, Amplitude, Firebase)

• Interest in investment, financial services, or cryptocurrency domains

Work Environment

Attractive compensation package with competitive salary and flexible bonus scheme. Individual career path with management and technical growth tracks, enhanced by learning and development programmes. Healthy work environment with company-sponsored medical programme, open communication, and friendly policies supporting work-life balance. We are an equal-opportunity employer and value diversity.