Machine Learning Engineer

2 days ago

Kuala Lumpur, Kuala Lumpur, Malaysia Outsized Full time 120,000 - 240,000 per year

*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

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