AI Data Engineer

About the Company

LG AI Research leads the AI transformation across industries through the research and development of large-scale AI models, including EXAONE. The Data Governance team within the Product Unit designs and builds integrated data platforms to manage data collection, refinement, versioning, and access control, enabling Practical AI applications.

Responsibilities

• Design Data Catalog systems (search, classification, quality, ownership) and standard schema/policy frameworks
• Implement data access control (permissions, policies, IAM-based tokens) and audit/tracking systems
• Develop SDK/CLI tools and integrate them into research workflows (training, evaluation, experiments)
• Deploy and operate Kubernetes-based Data Pipeline services, including CI/CD and automation
• Design and operate PB-scale storage and pipeline architectures on Google Cloud Platform
• Optimize performance, cost, and availability for large-scale data and multiple organizations based on SLO/SLA
• Design system monitoring/observability environments and enhance incident response systems

Requirements

• 7+ years of software engineering experience, including 3+ years in data infrastructure/platform and architecture design
• Experience designing and operating infrastructure on Google Cloud Platform
• Experience deploying and operating services on Kubernetes (including automation and incident response)
• Proficiency in Python-based backend/platform development and experience with large-scale system design
• Understanding and operational experience with distributed system design principles
• Experience designing and developing structured/unstructured data pipelines
• Proficiency in Linux environments and strong technical communication skills

Preferred Qualifications

• Experience establishing corporate data governance or data centralization projects
• Experience operating metadata catalogs/lineage/policy systems (e.g., Unity Catalog, DataHub, Hive Metastore)
• Experience handling diverse data types from service logs to AI research datasets (training sets, benchmarks, models)
• Understanding of LLM / Foundation Model training data pipelines
• Experience with data versioning and Lakehouse technologies (e.g., LakeFS, Delta Lake, Apache Iceberg)
• Experience with large-scale data processing frameworks (e.g., Apache Spark, Trino, Flink)
• Experience with IaC / GitOps-based infrastructure management (e.g., Helm, Terraform, ArgoCD)