AI Engineer

VISEO; Singapore; On-site; SGD 85,000 - 120,000; Full time

Job summary

A global technology solutions provider in Singapore is seeking an experienced AI Engineer to design and govern enterprise-grade AI solutions across multi-cloud environments. This role requires strong expertise in Microsoft technologies, proficiency in AI/ML frameworks, and hands-on experience in MLOps and data engineering. The ideal candidate will lead the development and operationalization of AI solutions, define comprehensive governance frameworks, and drive continuous improvement in AI delivery processes. Opportunities for mentoring and collaboration with cross-functional teams are integral to this role.

Qualifications

• 5+ years of experience in AI/ML architecture and machine learning development.
• Demonstrated experience with model lifecycle practices including training pipelines and CI/CD for ML.
• In-depth understanding of AI governance including bias detection and model explainability.

Responsibilities

• Design and deliver AI/ML solutions across multi-cloud environments.
• Define and enforce AI governance frameworks addressing compliance.
• Lead end-to-end development and operationalization of AI solutions.
• Own and manage production operations of AI systems.
• Drive continuous improvement in model performance and reliability.

Skills

Expertise in AI/ML frameworks

Proficiency in data engineering

Experience with MLOps

Strong understanding of Microsoft technologies

Familiarity with deep learning techniques

Education

Bachelor's or Master's degree in computer science, data science, information technology, or related field

Job description

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.

Roles and Responsibilities

• 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 explain ability.
• 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.

Profile

• 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.

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