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AI/ML Strategy & Roadmap:

• Define and execute the bank’s AI/ML strategy, creating a prioritized roadmap of initiatives that align with key business objectives in risk, customer service, and operational efficiency.
• Establish the vision and lead the implementation of the AI-ready Data Warehouse, ensuring it provides a clean, reliable, and feature- rich foundation for model training and inference.
• Act as the bank’s thought leader on AI/ML, educating stakeholders on capabilities, limitations, and ethical use of AI.
• End-to-End Model Development & MLOps:
• Lead the end-to-end lifecycle of AI/ML solutions, from data exploration and feature engineering to model training, validation, deployment, and monitoring.
• Architect, build, and train sophisticated ML models using Python and TensorFlow.
• Implement MLOps practices to automate the training, versioning, and deployment of models, ensuring reproducibility and scalability.

LLM Integration & Advanced AI Solutions:

• Strategically integrate Large Language Models (LLMs), both external (like OpenAI GPT, Claude) and internal, into the bank’s solutions to power advanced natural language understanding and generation.
• Design and implement patterns for effective LLM usage, including Retrieval-Augmented Generation (RAG) for knowledge base querying, prompt engineering, and fine-tuning for domain-specific tasks.
• Oversee the development of intelligent agent frameworks that can orchestrate complex tasks across multiple systems.

Team Leadership & Cross-Functional Collaboration:

• Build, manage, and mentor a high-performing team of Data Scientists, ML Engineers, and AI Specialists.
• Collaborate closely with the Manager, Business Analysis to define AI project requirements and success metrics.
• Partner with the Manager, Full Stack Engineering to productize models as scalable APIs and with the Manager, Solution Integration to ensure seamless data flow from source systems.
• Work with the Manager, Solution Quality Assurance & DevOps to establish rigorous testing for models (e.g., fairness, bias, drift) and to implement CI/CD for ML pipelines.

Governance, Risk & Ethics:

• Establish a robust model governance framework, including documentation, version control, and performance monitoring for all production models.
• Proactively identify and mitigate risks associated with AI, including model bias, data privacy, security, and ethical implications.
• Ensure all AI solutions comply with regulatory standards and the bank’s responsible AI principles.