The Opportunity.

We are seeking a Machine Learning Engineer to join a large-scale data transformation program for a major telecommunications client under a leading global consulting firm.

This role is responsible for model maintenance, automation of ML training pipelines, and end-to-end model lifecycle management in AzureML environments. The ML Engineer will work closely with data scientists and data engineers to productionize experimental models, ensure scalability, and maintain continuous optimization of model performance in production.

The Role.

Design, build, and maintain robust and scalable machine learning models using AzureML.
Develop reusable components for model training, evaluation, deployment, and monitoring.
Implement automated retraining and model performance tracking workflows to manage model drift and accuracy degradation.
Optimize models for accuracy, latency, scalability, and cost efficiency across environments.
Collaborate with data scientists and engineers to productionize models using modern DevOps and MLOps practices.
Deploy models through APIs or microservices, ensuring reliability, reproducibility, and maintainability.
Maintain technical documentation for model architectures, configurations, and performance metrics.

The Expertise.

Education & Certifications

Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or related field.
Certifications in Azure Machine Learning, MLOps, or Cloud Architecture are an advantage.

Experience & Background

3–6 years of experience in machine learning engineering or MLOps roles.
Proven hands-on experience in model development, automation, and deployment using AzureML or similar cloud platforms.
Strong programming skills in Python, with experience using ML frameworks such as LightGBM, Scikit-learn, or TensorFlow/PyTorch.
Solid understanding of model evaluation metrics, drift detection, and monitoring techniques.
Experience building CI/CD pipelines for ML workflows using MLflow, Kubeflow, Airflow, or SageMaker Pipelines.
Hands-on experience with model serving via APIs or microservices (Docker, Kubernetes).
Familiarity with data versioning, feature stores, and production monitoring best practices.

Must-Have Technical Skills

Python programming for ML model development
AzureML and/or other cloud ML platforms
MLOps tooling (MLflow, Kubeflow, Airflow, or equivalent)
Containerization (Docker, Kubernetes)
CI/CD for ML and model retraining automation
Model performance monitoring and drift detection

Preferred Skills

Experience in telecom, fintech, or digital transformation analytics projects.
Exposure to data pipeline orchestration tools and feature engineering workflows.
Strong collaboration skills working in cross-functional AI/ML and data teams.

Other Relevant Information

Location: Kuala Lumpur, Malaysia (Onsite at Client Site – KL Sentral)
Engagement Type: 12 months project-based
Schedule: Standard hours, complete onsite
Client Industry: Telecommunications / Digital Transformation
Start Date: December 2025 (2-week onboarding)

About us.

Thakral One is a consulting and technology services company headquartered in Singapore, with a pan-Asian presence. We focus primarily around technology-driven consulting, adoption of value-added bespoke solutions, enabling enhanced decision support through data analytics, and embracing possibilities in the cloud. We are heavily inclined towards building capabilities collaboratively with clients and believe strongly in improving grounded and practical outcomes. This approach is possible through our partnership with leading global technology providers and internal R&D teams. Our clients come from Financial Services, Banking, Telco, Government, Healthcare, and Consumer-oriented organisations.

Machine Learning (ML) Engineer (AzureML, Python) - Malaysia