Job Description:
AI / GenAI Solution Design & Delivery
- Lead the end-to-end design and implementation of AI and GenAI models for banking use cases.
- Deliver working GenAI-powered features embedded in prototype and production banking applications.
- Develop reusable AI components to accelerate future implementations.
- Document the full AI model lifecycle, from design and training to deployment and monitoring.
- Balance innovation with legacy system constraints to ensure practical, scalable solutions.
Enterprise Architecture Alignment & Integration
- Ensure AI solutions align with Enterprise Architecture (EA) principles and technology standards.
- Define and document AI architecture patterns that integrate seamlessly with existing legacy systems.
- Design scalable and secure deployment models for enterprise-wide adoption.
- Collaborate with EA, engineering, security, and product teams to manage dependencies and integration risks.
Responsible AI & Governance
- Promote responsible AI adoption, ensuring ethical, explainable, and compliant AI usage.
- Define AI governance, risk, and compliance standards in line with banking regulations.
- Monitor AI model performance, accuracy, and drift using dashboards and alerts.
- Address regulatory, ethical, and data privacy considerations throughout the AI lifecycle.
Job Requirements:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- Hands-on experience with Generative AI models and frameworks, including LangChain, Hugging Face, and OpenAI APIs.
- Strong understanding of Natural Language Processing (NLP), Large Language Models (LLMs), and prompt engineering techniques.
- Proficiency in Python, with experience using ML libraries such as PyTorch and/or TensorFlow; experience in custom AI model development is an advantage.
- Familiarity with Microsoft-native AI platforms, including Azure AI Foundry, Azure Cognitive Services, and Power Platform, for enterprise-grade AI integration and deployment.
- Solid knowledge of Azure Cloud Platform, including AI service deployment and integration patterns.
- Experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Understanding of MLOps practices, including model lifecycle management, monitoring, and CI/CD pipelines for AI solutions.
