Responsibilities

📍Develop and maintain in-house KYC and anti-fraud solutions, including ID forgery detection, document recognition and verification, and image and video-based fraud analysis. 📍Design, train, fine-tune, and evaluate computer vision and/or LLM-based models for fraud detection, covering image quality assessment, tampering and forgery artefacts, and AI-generated content or deepfake signals. 📍Analyse large-scale, unlabeled or weakly labelled datasets to identify suspicious patterns and generate features for manual review and model training. 📍Collaborate with engineering teams to deploy models into production pipelines, and work closely with Strategy and Operations teams to validate outputs and improve detection coverage. 📍Monitor model performance in production and continuously iterate to improve coverage while maintaining low false-positive rates.

Requirements

📍Bachelor’s Degree or above in Computer Science, Data Science, AI, or a related field 📍Strong experience in Python and common data science / ML libraries 📍Solid understanding of CV/ML/AI fundamentals 📍Experience with computer vision techniques (e.g. image preprocessing, feature extraction, OCR and etc) 📍Familiarity with model training, fine-tuning, and evaluation 📍Ability to work with large-scale datasets and conduct exploratory data analysis 📍Good problem-solving skills and ability to work independently in a fast-paced environment

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