Assist in the development and optimization of machine learning models.
Preprocess and analyze datasets to ensure data quality.
Collaborate with senior engineers and data scientists on model deployment.
Conduct experiments and run machine learning tests.
Stay updated with the latest advancements in machine learning.
Requirements
Strong proficiency in Python for data analysis, machine learning, and automation.
Solid understanding of supervised and unsupervised AI/machine learning methods (e.g., XGBoost, LightGBM, Random Forest, clustering, isolation forests, autoencoders, neural networks, transformer-based architectures).
Experience in payment fraud, AML, KYC, or broader risk modeling within fintech or financial institutions.
Experience developing and deploying ML models in production using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
Hands-on experience with LLMs (e.g., OpenAI, LLaMA, Claude, Mistral), including use of prompt engineering, retrieval-augmented generation (RAG), and agentic AI to support internal automation and risk workflows.
Ability to work cross-functionally with engineering, product, compliance, and operations teams.
Proven track record of translating complex ML insights into business actions or policy decisions.
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