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Tier-1 brand, metro role, mid-level ML title, and broad GenAI/MLOps requirements increase competition.
Specialized GenAI/agentic ML skills and regulated finance context limit cross-industry transferability.
Explicit 3+ years, mandatory agentic LLM experience, production MLOps, and specific tech proficiencies tighten filters.
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Design, develop, and deploy predictive ML, advanced analytics, GenAI/LLM, and agentic AI solutions to enhance control management processes across Consumer & Community Banking.
Create reusable, scalable AI services that integrate with business workflows for risk monitoring, regulatory compliance, issue identification, and governance within Control Management.
Own end-to-end ML model lifecycle including dataset engineering, training, deployment, MLOps practices, and collaboration with stakeholders for alignment and adoption.
Bachelor's degree in data science, computer science, statistics, mathematics, or related technical field, or equivalent practical experience.
3+ years experience or demonstrated ability setting up and deploying AI/ML solutions end-to-end (prototype to production).
Strong Python proficiency with hands-on experience in ML/deep learning libraries (PyTorch, TensorFlow, scikit-learn) and agentic AI (LLM-enabled workflows with evaluation methodology).
Experience with production ML/LLM pipelines, MLOps (versioning, CI/CD, monitoring), and cloud/container environments (AWS/Azure/GCP, Kubernetes).
Experienced practitioner in building and scaling agentic AI/GenAI systems with strong evaluation and guardrail practices.
Operationally skilled in deploying measurable AI services in complex, regulated financial services environments focused on risk and compliance.
Collaborates effectively with cross-functional stakeholders to translate business challenges into technical solutions and drives adoption through measurable impact.