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Tier-1 brand and metro location increase competition, but senior fraud-specialized ML role reduces applicant pool.
Fraud-focused ML engineering is highly domain-specific, limiting cross-industry transferability.
Mandatory years, ML/cloud stack, and fraud domain expertise create stringent hiring filters.
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Lead design, development, and optimization of machine learning models and data-driven initiatives to improve fraud detection and prevention at global scale.
Develop and maintain production ML pipelines, large-scale data processing solutions, and data products supporting risk decisioning.
Collaborate cross-functionally to integrate ML solutions into products, monitor performance, and mentor engineering teams on AI-assisted development tools.
5+ years relevant experience in Data Engineering, Data Science, Machine Learning Engineering, Fraud Analytics, Risk Modeling, or related fields.
Bachelor's degree or equivalent combination of education and experience.
Extensive experience with ML frameworks (TensorFlow, PyTorch, scikit-learn) and cloud platforms (AWS, Azure, GCP).
Strong skills in Python, SQL, large-scale data platforms, data processing, and model deployment.
Experienced in building scalable, secure machine learning-powered data products and robust data pipelines tailored for fraud risk and prevention use cases.
Proficient in modern AI technologies including Large Language Models, Agentic AI frameworks (e.g., LangChain, OpenAI Agents SDK), and AI automation workflows.
Demonstrates leadership in mentoring technical teams and driving adoption of advanced ML and data engineering best practices in high-volume transaction environments.