Job Description

Position Description

As a

Senior Machine Learning Engineer (Trading & Financial Intelligence)

, you will develop AI-powered systems and autonomous agents that transform how financial analysis and decision-making are conducted. You will build intelligent solutions that analyze markets, extract insights from complex financial data, and assist with risk management using advanced ML and quantitative techniques. This role offers the opportunity to apply cutting-edge AI, from traditional machine learning to modern LLM-based agents, to solve complex financial problems while collaborating with trading, research, and product teams.

What You Will Be Doing:

Design and implement machine learning solutions for financial markets, ranging from predictive models to autonomous AI agents powered by LLMs Develop intelligent systems using both traditional ML approaches (time series analysis, anomaly detection, pattern recognition) and modern agentic frameworks (LangChain, reasoning loops, tool orchestration) Apply quantitative methods and data mining techniques to extract actionable insights from large-scale financial datasets Build end-to-end ML pipelines for model development, backtesting, and production deployment with robust monitoring and evaluation frameworks Create research platforms that enable rapid experimentation with both classical statistical models and LLM-based approaches for financial analysis Collaborate with traders, quants, and researchers to translate complex financial problems into scalable ML solutions Develop risk assessment and portfolio optimization systems using a combination of traditional quantitative methods and AI-driven approaches

What You Need to Be Successful in This Role:

We welcome all applicants who are eligible to work in Singapore.

Bachelor's or Master's degree

in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Financial Engineering, or related quantitative field

3+ years of experience

in machine learning engineering, quantitative research, or data science with production systems

Strong programming skills

in Python with expertise in scientific computing libraries (pandas, numpy, scikit-learn) and ML frameworks

Experience with diverse ML techniques

including supervised/unsupervised learning, deep learning, time series forecasting, and statistical modeling

Familiarity with Large Language Models

and modern AI techniques, including prompt engineering, fine-tuning, and agentic systems is highly valued

Strong quantitative and analytical skills

with ability to apply mathematical and statistical concepts to real-world problems

Experience with data mining and feature engineering

from large, complex datasets

Problem-solving mindset

with ability to work independently and in fast-paced, results-oriented environments

Good communication skills

to present technical findings to both technical and non-technical stakeholders

Knowledge of financial markets, trading systems, or quantitative finance

is a plus but not required - we value strong technical skills and learning ability

Experience with backtesting frameworks, risk modeling, or portfolio optimization

is beneficial




https://pluanghrms.darwinbox.com/ms/candidate/careers/a68c3ceda55b16