*This is a 6-month contract with a possibility of an extension into a multi-year contract. Applicants must be fully onsite in Kuala Lumpur. Must have strong experience in AzureML/Azure Databricks and Python.
Project Overview
We are working with a top tier global consulting firm to support the delivery of a large scale transfomation for a Telecom client based out of Malaysia.
This is a 5 year programme focused on AI revenue transformation and acceleration. They now require practitioners with proven hands on experience across a number of expertise areas to join them to help scale and drive value out of what has been built in phase 1.
This is a hands-on engineering role requiring strong technical capability in model lifecycle management, ML pipelines, and Azure ML ecosystem.
Key Responsibilities
- Model Development & Operationalisation
- Design, build, and maintain production-grade machine learning models within AzureML.
- Translate data scientist prototype models into scalable, reliable production workflows.
- Develop reusable training, evaluation, validation, and deployment components.
- ML Pipeline Automation & MLOps
- Build and optimise end-to-end ML pipelines, including CI/CD for ML artefacts.
- Implement automated monitoring, drift detection, retraining, and rollback mechanisms.
- Set up and maintain model registries, feature stores, and experiment tracking.
- Model Serving & Performance Optimisation
- Deploy models via APIs or microservices (Docker, Kubernetes, FastAPI, etc.).
- Optimise models for accuracy, latency, stability, scalability, and cost efficiency.
- Troubleshoot production issues and proactively improve model reliability.
- Cross-functional Collaboration
- Work closely with data scientists, data engineers, product managers, and consulting teams.
- Participate in roadmap discussions to scale existing AI use cases across telecom domains (marketing, customer value management, pricing, network, etc.).
- Contribute to best practices, coding standards, and engineering guidelines for the ML platform.
Ideal Profile
- Technical Skills
- Strong programming experience in Python.
- Hands-on with ML frameworks such as LightGBM, xgBoost, scikit-learn.
- Solid understanding of:
- Model evaluation & monitoring
- Drift detection techniques
- Experiment tracking
- Feature store operations
- Automated retraining
- Experience in MLOps tools and pipeline orchestration (Azure preferred):
- Azure ML / Azure Databricks
- MLflow, Kubeflow, Airflow, or similar ML pipeline frameworks
- CI/CD pipelines for ML workloads
- Experience deploying ML models using:
- Docker
- Kubernetes
- API-based serving frameworks
