Quality Control jobs in Singapore

A highly skilled AI Engineer who leads the design, development, and governance of enterprise‑grade AI solutions across multi‑cloud environments, including Microsoft Azure and AWS. This role demands deep expertise in Microsoft technologies, strong proficiency in AI/ML frameworks, and hands‑on experience in data engineering, MLOps, and scalable architecture design. This role is ideal for someone who thrives at the intersection of AI innovation, cloud engineering, and enterprise strategy.

• Design and deliver enterprise‑grade AI/ML solutions across multi‑cloud environments (Azure, AWS), ensuring scalability, security, performance, and seamless integration with existing technology ecosystems.
• Define and enforce comprehensive AI governance frameworks addressing compliance (e.g., GDPR, EU AI Act), model risk, ethics, transparency, and explainability.
• Serve as a trusted advisor to business and technology leaders on AI strategy, emerging trends, and the long‑term impact of AI on enterprise processes, platforms, and decision‑making.
• Lead end‑to‑end development and operationalization of AI solutions, including data exploration, model development, training, validation, deployment, and lifecycle management.
• Own and manage production operations of AI systems—covering monitoring, incident management, CI/CD pipelines, and release/change controls.
• Assess and evaluate change requests, effort, and impact across business functions, technology platforms, and governance controls to ensure strategic alignment and risk mitigation.
• Drive continuous improvement in model performance, system reliability, and AI delivery processes through rigorous testing, automation, and adherence to engineering best practices.
• Partner with cross‑functional teams (data engineering, application development, infrastructure, business units) to translate complex business challenges into actionable AI‑driven solutions.
• Champion a culture of technical excellence and knowledge sharing by mentoring peers, reviewing code and architecture, and contributing to internal AI communities of practice.
• Other duties as required.

• Bachelor's or Master's degree in computer science, data science, information technology, or a related field, with 5+ years of experience in both AI/ML architecture and hands‑on machine learning development.
• Strong foundation in machine learning and familiarity with deep learning techniques and frameworks.
• Demonstrated experience with MLOps and model lifecycle practices (training pipelines, monitoring, retraining, and CI/CD for ML).
• In‑depth understanding of AI governance and responsible AI practices, including bias detection, model explainability, and alignment with global regulatory standards.