Proven hands-on delivery experience in banking, financial institutions, or insurance within Gen AI solutions such as chatbots, document analysis, etc., leveraging RAG and robust architecture with proper governance and security measures
Several years of ML experience with implemented use cases.
Hands-on work experience most of which in banking, financial institutions, or insurance industries.
Experience required:
Ability to build and deploy ML models using Python and relevant libraries. Understanding of supervised and unsupervised learning algorithms.
Experience with model evaluation and performance metrics.
Familiarity with AI use cases in banking (e.g., fraud detection, personalization) Knowledge of data preprocessing and feature engineering.
Ability to work with cloud-based ML platforms (e.g., Azure ML, AWS SageMaker). Understanding of MLOps and model lifecycle management.
Ability to communicate insights and build explainable AI models.
Joining time frame: (15 - 30 days)
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