MLOps AI Engineer

We are currently seeking a MLOps AI Engineer to join a global end client, supporting the delivery of scalable, production-ready AI and machine learning solutions.

We are ideally looking for someone on a permanent basis, although contract-to-permanent could also be considered.

Location: Hybrid – remote with 1 day per week onsite in Kuala Lumpur, Malaysia

Experience Required:

• 1-3 years experience
• Proven experience delivering ML/Analytic solutions, working across Data disciplines.
• Hands-on experience operating Databricks or comparable lakehouse platforms, including runtime upgrades, workspace administration, access controls.
• Experience implementing MLOps practices across the model lifecycle (CI/CD, versioning, monitoring, reproducibility)
• Experience working with security, risk, and governance teams to evidence controls for data and AI services.
• Hands-on experience with core ML engineering tooling and practices (e.g., Python packaging, Git, CI/CD, automated testing, and containerisation/serving patterns where applicable).
• Strong communication and collaboration skills; able to work effectively with data scientists, data engineers, product and risk stakeholders to deliver production outcomes.

Key Responsibilities:

• Design, build, and maintain end-to-end machine learning pipelines on Databricks, covering data preparation, feature engineering, model training, evaluation, and deployment.
• Develop production-grade Python and Spark solutions that adhere to software engineering best practices, secure coding standards, and code review processes.
• Implement and manage MLOps capabilities, including MLflow, model versioning, lifecycle management, reproducibility, and deployment governance.
• Build and maintain CI/CD pipelines for machine learning solutions, enabling automated testing, packaging, deployment, and environment management.
• Monitor production ML models and data pipelines, proactively addressing performance, data quality, model drift, latency, and operational issues.
• Collaborate with data engineering, data science, security, and governance teams to deliver reliable, scalable, and compliant AI/ML solutions.
• Apply security, risk, and compliance controls, supporting audits and maintaining documentation, traceability, and operational standards for AI services.
• Drive continuous improvement by optimising Databricks workloads, developing reusable ML engineering components, and sharing best practices across teams.

If you're interested or would like to find out more, please send your latest CV and we'll be in touch with further details.