Machine Learning Engineer

Position DescriptionAs a Machine Learning Engineer (Trading & Financial Intelligence), you will contribute to the development of AI-powered systems and autonomous agents that transform how financial analysis and decision-making are conducted. Working under the guidance of senior team members, you will help build intelligent solutions that analyze markets, extract insights from financial data, and support risk management using machine learning and quantitative techniques. This role offers an excellent opportunity to learn and apply both traditional ML and modern LLM-based approaches to solve real financial problems while collaborating with experienced trading, research, and product teams.What You Will Be Doing:Assist in designing and implementing machine learning solutions for financial markets, from predictive models to AI agents powered by LLMsSupport the development of intelligent systems using traditional ML approaches (time series analysis, anomaly detection, pattern recognition) and modern agentic frameworksHelp apply quantitative methods and data mining techniques to extract insights from financial datasets under senior guidanceContribute to building ML pipelines for model development, backtesting, and production deployment with monitoring frameworksSupport research platforms that enable experimentation with both classical statistical models and LLM-based approaches for financial analysisWork closely with traders, quants, researchers, and senior engineers to understand and help solve complex financial problemsAssist in developing risk assessment and portfolio optimization systems using quantitative methods and AI-driven approachesParticipate in code reviews, documentation, and knowledge sharing to continuously improve technical skillsWhat You Need to Be Successful in This Role:Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Financial Engineering, or related quantitative field2+ years of professional experience in machine learning, data science, or software engineering (internships, projects, and academic experience count)Solid programming skills in Python with familiarity with scientific computing libraries (pandas, numpy, scikit-learn)Foundational knowledge of machine learning including supervised/unsupervised learning, basic deep learning concepts, and statistical modelingInterest in Large Language Models and modern AI techniques - experience with prompt engineering, fine-tuning, or agentic systems is a plus but not requiredStrong mathematical and analytical foundation with ability to learn and apply quantitative concepts to practical problemsExperience with data manipulation and basic feature engineering from structured datasetsEagerness to learn with ability to work collaboratively in a mentorship-oriented environmentGood communication skills to discuss technical concepts and ask questions effectivelyBasic understanding of software engineering practices including version control (Git) and testingCuriosity about financial markets - prior knowledge of trading systems or quantitative finance is beneficial but not requiredAcademic or personal projects demonstrating ML skills through coursework, competitions, or self-directed learning