Job
Description
*Design and deploy ML models* to score trader performance, risk exposure, and funding eligibility in real-time. Build AI systems to: * Auto-journal trades and classify PnL events * Detect trader manipulation, bots, or copy trading behavior * Identify high-potential traders early (funding prioritization) Use LLMs and NLP to create smart summaries, trade notes, and insights from user behavior and support queries. Own the *AI infrastructure*, from experimentation to production (Docker, APIs, CI/CD). Collaborate closely with our backend, frontend, and R\&D teams to bring AI features into the trader dashboards and CRM. Constantly explore new AI tools, algorithms, and research relevant to fintech and trading ecosystems. *Ideal Candidate Profile* *4+ years of hands-on experience* in building and deploying ML/AI models in production environments. Solid foundation in *machine learning, deep learning, and statistics*. Expertise with tools like *Python, PyTorch/TensorFlow, Scikit-learn, XGBoost, HuggingFace*, etc. Strong data pipeline and API experience (Airflow, FastAPI/Flask, Pandas, SQL, MongoDB). Comfortable with deploying ML models at scale using *Docker, MLflow, AWS/GCP*. Bonus: Experience in *fintech, trading platforms, fraud detection, or real-time data systems*. Bonus++: Knowledge of reinforcement learning, anomaly detection, or building recommender systems. --- What Youll Build (Example Projects) *Traderscore AI*: A real-time rating engine to predict trader potential and risk based on behavior patterns. *AnomalyBot*: A system that flags suspicious trades and prevents fraud before payout. *Smart Journal*: NLP-powered auto trade logs with emotional sentiment tracking and decision reviews. *TopTraderRadar*: Predictive analytics dashboard to identify breakout talent globally.
