Full Time Solutions Engineer Jobs, in Maybank Kuala Lumpur - Ricebowl

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.