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Open nowPosted today

AI Engineer

MyCareersFuture94,028 open roles

Pay
SGD 8,000 – SGD 9,600 a month
Where
Central, Singapore
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Your applicationOpen nowAI EngineerMyCareersFuture · Central, Singapore

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on MyCareersFuture's own form.

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The clock on this job

Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 1.6%1 day
  2. 3.4%3 days
  3. 7.9%7 days
  4. 14.2%14 days
  5. 34.1%30 days
This job: posted today

The posting

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; escalate 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.
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