Senior AI Engineer: GenAI, MLOps & Platform Delivery

Kenanga Group

Kuala Lumpur

Vor Ort

MYR 180.000 - 300.000

Vollzeit

vor 14 Stunden
Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

Kenanga Group in Kuala Lumpur seeks an AI Engineer to embed Generative AI capabilities across digital platforms. You will design, develop, deploy, and continuously improve AI and GenAI solutions to drive business impact and efficiency.

You will implement CI/CD, containerization and Kubernetes, manage model versioning, and align with enterprise standards while addressing governance and regulatory requirements. Ongoing learning and documentation are expected.

Qualifikationen

  • Degree in Computer Science, AI, Machine Learning, Data Science or related field
  • 4-8 years on platform administration field
  • Hands-on experience delivering AI / ML solutions from ideation to production

Aufgaben

  • Design and deliver AI/GenAI solutions including NLP, recommendation systems, and multi-modal models.
  • Implement CI/CD, containerization, Kubernetes, and model versioning aligned to enterprise standards.
  • Define and evolve GenAI strategy, roadmap, and ethical adoption. Engage senior stakeholders and secure sponsorship.
  • Build, deploy and monitor generative models; integrate models into production platforms with feedback loops
  • Ensure AI lifecycle governance, privacy protection, and regulatory alignment.
  • Attend relevant trainings and compulsory courses; document solutions and share best practices.

Kenntnisse

AI/ML solutions
NLP
MLOps
Kubernetes
CI/CD
Model versioning
Data governance

Ausbildung

Bachelor's degree in CS/AI/ML

Jobbeschreibung

Kenanga Group in Kuala Lumpur seeks an AI Engineer to embed Generative AI capabilities across digital platforms. You will design, develop, deploy, and continuously improve AI and GenAI solutions to drive business impact and efficiency.

You will implement CI/CD, containerization and Kubernetes, manage model versioning, and align with enterprise standards while addressing governance and regulatory requirements. Ongoing learning and documentation are expected.

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