Posted:15 hours ago|
Platform:
On-site
Full Time
You will lead the design of AI systems that assess banking health and predict risk trends. The role involves implementing scalable AI workflows and integrating multiple data sources for robust risk analytics and early warning signals.
· Lead implementation of predictive ML models for capital adequacy, liquidity risk, and market indicators.
· Use NLP to analyse regulatory filings, contracts, and KYC documents.
· Automate stress testing and back-testing using AI.
· Manage integration with internal dashboards and resolution management tools.
· Mentor junior engineers and contribute to data governance best practices.
· Design AI systems that process transactional and behavioral banking data (loans, accounts, transfers, payments).
· Create scalable pipelines with MLflow for experiment tracking and reproducibility.
· Implement deep learning models (LSTM, CNN, BERT) for time series forecasting and document summarization.
· Work with MLOps tools for continuous deployment, CI/CD, and real-time monitoring.
· Languages: Python, SQL, PySpark
· ML & Deep Learning: TensorFlow, PyTorch, Prophet, HuggingFace Transformers
· Big Data: Apache Spark, Delta Lake, Databricks
· MLOps: MLflow, Airflow, Docker, GitHub Actions
· Platforms: Azure ML, SageMaker, Databricks
· Domain: Basel II/III, banking KPIs, early warning indicators
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