MLOps AI Engineer

DSR Global; Kuala Lumpur; Hybrid; MYR 90,000 - 180,000; Full time

Job summary

DSR Global is seeking an MLOps AI Engineer to join a global client delivering scalable, production-grade AI and ML solutions. The role is permanent with a hybrid work model: remote with one day onsite weekly in Kuala Lumpur, Malaysia.

You will design end-to-end ML pipelines on Databricks, implement MLOps across the lifecycle, and collaborate with cross-functional teams to ensure secure, compliant deployments. 1–3 years of relevant experience required.

Qualifications

• 1–3 years of ML/analytic experience required.
• Hands-on with Databricks or comparable lakehouse platforms.
• Experience implementing MLOps across the model lifecycle (CI/CD, versioning, monitoring).
• Collaborate with data scientists, data engineers, product and risk teams to deliver production AI/ML.

Responsibilities

• Design and maintain end-to-end ML pipelines on Databricks (data prep, feature engineering, training, deployment).
• Develop production-grade Python/Spark solutions with software engineering best practices.
• Implement and manage MLflow, model versioning, and deployment governance.
• Build and maintain CI/CD pipelines for ML solutions with automated testing and environment mgmt.
• Monitor production models for performance, data quality, and drift.

Skills

MLOps

Python

Spark

CI/CD

Model lifecycle

Security & governance

Tools

Databricks

MLflow

Git

CI/CD tools

Job description

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.