AI/ML Developer

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We are looking for AI/ML Engineer with 8 to 10 years of experience.

Job Summary:

Work with business stakeholders, data scientists, and product managers to understand challenges and define clear requirements for AI-driven solutions.
Architect and design scalable, secure, and cost-optimized machine learning solutions on GCP using a wide range of services, especially within Vertex AI.
Build and code the core AI programs, machine learning models, and the applications that leverage them. Use languages like Python, Java, or Scala, along with frameworks such as TensorFlow, PyTorch, and scikit-learn.
Utilize generative AI, large language models (LLMs), and retrieval-augmented generation (RAG) to build features like chatbots or content summarization.
Create pipelines for ingesting, cleaning, and transforming raw data into a format suitable for training ML models. This often involves services like Dataflow, Dataproc, and BigQuery.
Use Vertex AI Pipelines to automate and orchestrate the end-to-end machine learning lifecycle, making the processes repeatable and auditable.
Implement MLOps practices for continuous integration/continuous delivery (CI/CD) of machine learning models. Automate deployment, versioning, and testing processes using tools like Cloud Build and Vertex AI Model Registry.
Store model artifacts in Cloud Storage and use Vertex ML Metadata to track and organize the lineage of ML resources.
Deploy trained models to production using online or batch prediction services on Vertex AI.
Set up monitoring systems to detect model performance degradation, such as feature skew or data drift, and fine-tune alert thresholds.

Details

Location

Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia Malaysia

Experience

Min. 8 yearsWe are looking for AI/ML Engineer with 8 to 10 years of experience.

Technologies

We are looking for AI/ML Engineer with 8 to 10 years of experience.

Job Summary:

Work with business stakeholders, data scientists, and product managers to understand challenges and define clear requirements for AI-driven solutions.
Architect and design scalable, secure, and cost-optimized machine learning solutions on GCP using a wide range of services, especially within Vertex AI.
Build and code the core AI programs, machine learning models, and the applications that leverage them. Use languages like Python, Java, or Scala, along with frameworks such as TensorFlow, PyTorch, and scikit-learn.
Utilize generative AI, large language models (LLMs), and retrieval-augmented generation (RAG) to build features like chatbots or content summarization.
Create pipelines for ingesting, cleaning, and transforming raw data into a format suitable for training ML models. This often involves services like Dataflow, Dataproc, and BigQuery.
Use Vertex AI Pipelines to automate and orchestrate the end-to-end machine learning lifecycle, making the processes repeatable and auditable.
Implement MLOps practices for continuous integration/continuous delivery (CI/CD) of machine learning models. Automate deployment, versioning, and testing processes using tools like Cloud Build and Vertex AI Model Registry.
Store model artifacts in Cloud Storage and use Vertex ML Metadata to track and organize the lineage of ML resources.
Deploy trained models to production using online or batch prediction services on Vertex AI.
Set up monitoring systems to detect model performance degradation, such as feature skew or data drift, and fine-tune alert thresholds.