AI Architect – Singapore

Pan Asia Group; Singapore; Presencial; SGD 180.000 - 240.000; Jornada completa

Descripción de la vacante

Pan Asia Group is seeking an AI Architect to design end-to-end AI/ML architectures across data pipelines, model training and deployment. You will bridge business needs with scalable, secure systems and set the technical direction for complex AI initiatives.

The role requires cloud expertise, strong ML/DL knowledge, and hands-on guidance for data scientists and ML engineers. Leadership and governance are key.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Cloud AI Platform

ML Algorithms

DL Frameworks

Team Leadership

Educación

Master's or PhD in CS/DS

Herramientas

AWS

Azure

GCP

Descripción del empleo

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.