AI Architect – Singapore

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.