MLOps Data Engineer (MLOps DE)

Overview

Boost Federal Territory of Kuala Lumpur, Malaysia

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The role will be responsible to bridge the gap between data science and IT operations, enabling seamless model lifecycle management and scalable ML infrastructure. Apart from that, the role is also responsible for designing, developing and optimizing data pipelines, ensuring reliable and efficient data workflows across the organization for structure and unstructured data related to Boost’s use cases and mainly to support Boost’s to incorporate unified data modelling across AI, Machine learning, and Analytics projects. This role will collaborate with cross-functional teams, including data scientists, analysts, software engineers and DevOps to optimize the production and deployment of machine learning solutions, and support and enhance data-driven decision-making and analytics.

Note : This refined description preserves the core responsibilities and qualifications from the original content while removing unrelated postings and boilerplate.

Responsibilities

• Develop and automate deployment pipelines for machine learning models, ensuring smooth transition from development to production.
• Implement monitoring systems to track model performance and stability, and address model drift or data drift issues.
• Design, build, manage and maintain end-to-end model lifecycle processes including pipelines for ML models, CI / CD, performance evaluation and monitoring, experiments, observability, deployments, versioning and serving.
• Manage and optimize ML infrastructure and cloud platforms (e.g., AWS, GCP, Azure, Databricks, Snowflake).
• Collaborate with data scientists and DevOps to support model scalability, reliability, and reproducibility in production.
• Maintain documentation for model pipelines, infrastructure, and deployment procedures to ensure consistency and compliance.
• Design, develop, manage and maintain scalable ETL / ELT data pipelines to support ingestion, transformation, and integration of data from multiple sources.
• Design and implement robust data architectures, data models, and storage solutions to ensure efficient data processing and accessibility.
• Optimize and manage data warehouses, ensuring high availability, reliability, and performance. Implement data quality checks, monitoring, and governance protocols to maintain data accuracy, consistency, and integrity, including access management.
• Identify and address performance bottlenecks in implemented solutions.
• Maintain thorough documentation of data pipelines, catalogs, workflows and lineage for transparency and reproducibility.
• Support the centralized Data Platform initiative to create a single view of Boost users and merchants.
• Automate data pipelines to reduce manual effort and improve efficiency.
• Conform to best practices when designing or implementing a solution.

Qualifications

Seniorities

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