AI Engineer

IDC TECHNOLOGIES (SINGAPORE) PTE. LTD.; Singapore; On-site; SGD 210,000 - 280,000; Full time

Job summary

IDC TECHNOLOGIES (SINGAPORE) PTE. LTD. seeks an experienced AI Model Upgrade Leader to own the end-to-end roadmap and execution of upgrades across ML, Generative AI, LLMs, and related services.

You will assess model versions for business needs, performance, security, and governance, define testing criteria, and lead migration, rollout, and post-implementation validation. The role emphasizes responsible AI, risk management, and audit-ready artefacts.

Qualifications

• 12–16 years of relevant experience in software engineering, AI/ML engineering, data engineering, platform engineering, or related technology roles.
• Significant experience leading enterprise-scale AI or ML platforms and production services.
• Demonstrated experience delivering and operating AI applications in regulated environments.
• Strong track record of leading multidisciplinary technical teams and managing senior stakeholders, external vendors, and technology partners.

Responsibilities

• Own the end-to-end roadmap and execution of upgrades across machine learning, Generative AI, large language model, and related AI services.
• Assess new model versions against business needs, performance, security, compatibility, and governance requirements.
• Define evaluation criteria and oversee benchmark, regression, safety, performance, and business acceptance testing before production release.
• Lead migration and rollout strategies, including release sequencing, rollback planning, change controls, and post-implementation validation.
• Ensure prompts, configurations, datasets, evaluation assets, and model versions are controlled, traceable, and documented.
• Lead upgrades to application frameworks, libraries, APIs, runtime environments, cloud services, data components, and AI/ML platforms.
• Evaluate technical debt, dependencies, and end-of-life risks; translate findings into remediation plans.
• Partner with enterprise architecture and infrastructure teams to align with technology strategy and production standards.
• Embed responsible AI, model risk, cybersecurity, data privacy, change management, and audit requirements throughout the upgrade lifecycle.
• Maintain complete technical artefacts and evidence, including architecture decisions, model cards, test results, approvals, release records, and operational procedures.
• Identify and manage technical, model, operational, and third-party risks; drive remediation.
• Lead investigation and resolution of complex production incidents, including root cause analysis, corrective actions, and preventive improvements.

Skills

AI/ML leadership

Data engineering

Platform engineering

Leadership

Stakeholder management

Education

Bachelor’s or Master’s degree in CS/AI/DS/Engineering/IT

Tools

Python

AI/ML frameworks

Model APIs

CI/CD pipelines

Containerisation

Infrastructure-as-code

MLOps / LLMOps

Job description

Key Responsibilities:

AI Model Upgrade Leadership

• Own the end-to-end roadmap and execution of upgrades across machine learning, Generative AI, large language model, and related AI services.
• Assess new model versions, and capabilities against business needs, performance, security, compatibility, and governance requirements.
• Define evaluation criteria and oversee benchmark, regression, safety, performance, and business acceptance testing before production release.
• Lead migration and rollout strategies, including release sequencing, rollback planning, change controls, and post-implementation validation.
• Ensure prompts, configurations, datasets, evaluation assets, and model versions are controlled, traceable, and appropriately documented.

Technology Modernisation & Platform Upgrades

• Lead upgrades to application frameworks, libraries, APIs, runtime environments, cloud services, data components, and AI/ML platforms supporting departmental AI services.
• Evaluate technical debt, dependencies, and end-of-life risks; translate findings into prioritised remediation plans.
• Partner with enterprise architecture and infrastructure teams to ensure solutions align with technology strategy and production standards.

Governance, Risk & Controls

• Embed responsible AI, model risk, cybersecurity, data privacy, change management, and audit requirements throughout the upgrade lifecycle.
• Maintain complete technical artefacts and evidence, including architecture decisions, model cards, test results, approvals, release records, and operational procedures.
• Identify and manage technical, model, operational, and third-party risks; escape material issues and drive timely remediation.
• Lead investigation and resolution of complex production incidents, including root cause analysis, corrective actions, and preventive improvements.

Requirements

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Technology, or a related discipline.
• 12–16 years of relevant experience in software engineering, AI/ML engineering, data engineering, platform engineering, or related technology roles.
• Significant experience leading enterprise-scale AI or ML platforms and production services, including complex model and technology upgrades.
• Demonstrated experience delivering and operating AI applications in large, regulated, or operationally critical environments.
• Strong track record of leading multidisciplinary technical teams and managing senior stakeholders, external vendors, and technology partners.

Technical Skills

• Deep understanding of the AI/ML lifecycle, including model evaluation, deployment, versioning, monitoring, retraining, rollback, and retirement.
• Strong knowledge of Generative AI, large language models, retrieval-augmented generation, prompt engineering, agentic workflows, and responsible AI considerations.
• Proficiency in Python and experience with common AI/ML frameworks, model APIs, orchestration tools, and data processing libraries.
• Hands-on experience with MLOps or LLMOps platforms, CI/CD pipelines, model registries, automated evaluation, containerisation, and infrastructure-as-code.
• Strong knowledge of APIs, microservices, distributed systems, databases, vector stores, identity and access controls, security, logging, and observability.
• Ability to diagnose model and system performance issues across application, data, infrastructure, and integration layers.
• This role is for one of our project requirements

Preferred Qualifications

• Experience in banking, financial services, operations, or another highly regulated industry.
• Experience implementing model governance, AI risk controls, security reviews, audit evidence, and formal change management processes.
• Relevant certifications in cloud architecture, AI/ML engineering, cybersecurity, DevOps, or technology delivery.
• Experience managing multi-model or multi-provider AI environments and evaluating emerging AI technologies for enterprise adoption.