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Strong employer, popular ML/AI title, metro location, and broad skillset increase competition.
High because banking operations, AI governance, and regulatory controls demand domain-specific experience.
High due to extensive mandatory ML tooling, production experience, and AI governance requirements.
Design, develop, and implement AI solutions to improve Corporate and Investment Banking Operations and Controls, focusing on efficiency, cost savings, and revenue growth.
Collaborate with stakeholders including IT (TDI), product managers, and data experts to build scalable data products, AI models, and workflow automations using both no-code/low-code and advanced AI development tools.
Ensure AI governance, compliance with bank policies, thorough testing, validation, monitoring, and tracking of KPIs for all AI solutions deployed.
Experience delivering AI/Machine Learning models and solutions in a production environment, preferably within a Tier 1 or G-SIB bank.
Proficient with Data Science/AI development tools/platforms such as Python (including libraries like PyTorch, TensorFlow, scikit-learn), MLflow, Jupyter, and GitHub.
Experience with no-code/low-code AI tools (e.g., Microsoft AI Builder, Google AI Studio) and data platforms like BigQuery, Snowflake, Databricks, MongoDB, etc.
Work Experience Required: Not explicitly mentioned in the JD. Notice Period: Not explicitly mentioned in the JD.
Strong expertise in AI solution lifecycle management including risk management, testing, validation, access controls, and governance in a banking environment.
Operates effectively in a multi-stakeholder cross-geography team environment involving collaboration with IT and business units for end-to-end AI solution delivery.
Demonstrated ability to prioritize AI use cases rigorously based on ROI, strategic alignment, feasibility, and risk, showing an analytical and outcome-focused orientation.