AI Architect – Singapore
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The AI Architect is responsible for designing and overseeing the end-to-end architecture of
complex Artificial Intelligence and Machine Learning solutions. This role bridges the gap
between high-level business requirements and the technical implementation of scalable,
reliable, and secure AI systems.
Key Responsibilities
Architectural Blueprint: Design the complete technical stack for AI solutions, including
data acquisition, processing pipelines, model training environments, and
deployment/inference services.
Technology Evaluation: Evaluate and select appropriate AI/ML frameworks, cloud
services (AWS SageMaker, Azure ML, GCP Vertex AI), and infrastructure components
to meet performance needs.
MLOps Strategy: Define MLOps practices, including continuous integration and
continuous deployment (CI/CD) for models, monitoring model drift, and ensuring
reproducibility.
Security and Governance: Ensure all AI systems adhere to security protocols, data
privacy regulations, and ethical AI guidelines.
Team Leadership: Guide and mentor Data Scientists and ML Engineers on
architectural decisions, performance tuning, and best coding practices.
Required Qualifications
Master’s degree or Ph.D. in Computer Science, Data Science, or a related technical
discipline is preferred.
8+ years of experience in software development or data engineering, with 3+ years
specifically as an ML or AI Architect.
Deep expertise in a major cloud AI platform (AWS, Azure, or GCP) and related services.
Expertise in machine learning algorithms, deep learning frameworks (e.g., TensorFlow,
PyTorch), and model deployment methodologies.
Required Qualifications
Master’s degree or Ph.D. in Computer Science, Data Science, or a related technical
discipline is preferred.
8+ years of experience in software development or data engineering, with 3+ years
specifically as an ML or AI Architect.
Deep expertise in a major cloud AI platform (AWS, Azure, or GCP) and related services.
Expertise in machine learning algorithms, deep learning frameworks (e.g., TensorFlow,
PyTorch), and model deployment methodologies.