Machine Learning Lead - up to RM 15k

Randstad Malaysia; Kuala Lumpur; Hybrid; MYR 120,000 - 240,000; Full time

Job summary

Randstad Malaysia seeks an experienced ML Engineer to architect, build, and deploy machine learning models end-to-end within a hybrid work setup in Kuala Lumpur. You will lead Azure-based pipelines, translate business needs into ML strategies, and drive governance across the model lifecycle.

The ideal candidate has 4+ years in ML/DS/AI, strong Python/SQL skills, and hands-on Azure ML experience, with a track record of implementing custom models and collaborating with stakeholders.

Qualifications

• 4+ years of hands-on experience in ML, Data Science, or AI Engineering.
• Proven track record of building and training custom ML models from scratch.
• Deep experience deploying models on Microsoft Azure (Azure ML, Synapse).
• Exposure to AI governance frameworks, model risk management, and ethical AI practices.
• Strong communication to influence business decisions and align stakeholders.
• Proficiency in Python, SQL and ML frameworks (Scikit-Learn, TensorFlow, PyTorch).

Responsibilities

• End-to-End Model Development: Architect, build, and deploy ML models from scratch.
• Azure ML Architecture: Lead the deployment of scalable ML pipelines on Azure.
• Stakeholder Management: Bridge between technical teams and business units.
• AI Governance & Compliance: Ensure models meet security, bias, and compliance standards.
• Technical Execution: Manage data extraction, EDA, feature engineering, training, tuning, and monitoring.

Skills

Python

SQL

ML model dev

Azure ML

TensorFlow

PyTorch

Scikit-Learn

Tools

Azure Machine Learning

Azure Synapse

Databricks

Snowflake

Power BI

Job description

• Hybrid Work Arrangement
• Global Presence
• Great Benefits

About the company

Randstad has recently partnered with a digital solutions and consulting company, promoting growth and scaling for businesses and organisations on a global scale. Your future employers value the partnership of modern technology with reliable solutions.

Key Responsibilities:

• End-to-End Model Development: Architect, build, and deploy machine learning models completely from scratch to address complex, high-impact business challenges.
• Azure ML Architecture: Lead the technical execution and deployment of scalable machine learning pipelines within our Microsoft Azure ecosystem.
• Stakeholder Management: Act as the primary bridge between technical teams and business units. Translate business requirements into technical ML strategies and communicate complex model outcomes to non-technical leadership.
• AI Governance & Compliance: Implement and champion AI governance best practices. Ensure all models adhere to internal security, ethical, bias, and compliance standards throughout the model lifecycle.
• Technical Execution: Drive all phases of the machine learning lifecycle, including data extraction, exploratory data analysis, feature engineering, model training, tuning, and production monitoring.

Requirements:

• Experience: 4+ years of professional, hands-on experience in Machine Learning, Data Science, or AI Engineering.
• Custom Model Building: Proven track record of building and training custom ML models from scratch (beyond implementing off-the-shelf APIs or pre-trained models).
• Cloud Infrastructure: Deep, practical experience developing and deploying models on Microsoft Azure (e.g., Azure Machine Learning, Azure Synapse).
• AI Governance: Demonstrated exposure to AI governance frameworks, model risk management, and ethical AI practices.
• Communication & Stakeholder Alignment: Exceptional stakeholder management skills with a proven ability to influence business decisions, manage expectations, and drive cross-functional alignment.
• Technical Stack: Advanced proficiency in Python, SQL, and industry-standard machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch).

Nice to haves:

• Hands-on experience working with Databricks and/or Snowflake.
• Prior experience in a leadership capacity, whether through formal team management, project squad leadership, or actively mentoring junior data scientists and engineers.