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Strong Tier-1 brand, mid-level ML role, hybrid and multi-skill AI/data requirements.
Requires finance/payments domain knowledge and model risk governance, reducing cross-industry transferability.
Explicit 6+ years and many mandatory ML, data engineering, MLOps, and regulatory requirements.
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Design, build, and operationalize AI/ML anomaly detection and risk scoring models for high-volume payment flows.
Develop and maintain scalable batch, streaming, and near-real-time data pipelines supporting detection, alerting, and investigation workflows.
Implement and monitor AI/ML orchestration integrating GenAI, RAG, agentic AI, governance, and explainability within a regulated financial services environment.
6+ years professional experience; Bachelor's or Master's in Computer Science, AI/ML or related field.
3–5 years experience in Data Engineering, AI/ML Engineering, Software Engineering, or related disciplines.
Strong Python proficiency, experience with SQL, APIs, cloud-native architectures, MLOps, Docker, Kubernetes, and CI/CD pipelines.
Experience with AI techniques including anomaly detection models, GenAI, RAG, vector databases, and payment domain data engineering.
Technical expertise in anomaly detection and AI orchestration for payment risk and fraud detection in financial services.
Experience collaborating with cross-functional teams including risk, compliance, cybersecurity, and engineering to translate business needs into AI solutions.
Ability to implement and manage secure, compliant AI systems with governance, explainability, and operational monitoring in regulated environments.