Responsibilities

📍Develop and implement AI/ML models and tools to enhance data and risk analytics, portfolio risk analysis, real-time monitoring, and decision-making automation; 📍Analyze large-scale structured and unstructured datasets including trading activity, market data, news, and regulatory filings to detect patterns, anomalies, and emerging risks; 📍Drive the design and deployment of generative AI, large language models (LLMs), and NLP solutions for risk reporting, trader behavior analysis, and regulatory intelligence; 📍Collaborate with risk managers, quantitative analysts, and engineering teams to integrate scalable, production-grade AI systems and tools into the existing systems; 📍Drive applied research initiatives in cutting-edge AI to continuously improve data and risk management capabilities.

Requirements

📍3–5 years of hands-on experience developing and deploying AI/ML solutions, including LLMs, NLP, or agent-based systems (minimum 1 year required); 📍Strong programming skills in Python and SQL, with experience in data science libraries (e.g., Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow); 📍Solid background in quantitative finance, statistics, or applied mathematics; familiarity with trading data, derivatives, and market microstructure is a plus; 📍Experience deploying models in real-time or production environments; familiarity with cloud platforms and regulatory frameworks (e.g., CFTC, SEC) is a plus; 📍Proven ability to translate research and emerging AI technologies (including generative AI and LLMs) into practical, scalable solutions; 📍Effective collaborator with excellent problem-solving skills and the ability to work across technical, quantitative, and business teams.

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