Data Engineer | AI R&D Department

About the role

You will build a secure, traceable, and reusable AI‑ready data foundation to support retrieval‑augmented generation, model training, analytics, and real‑world AI workflow implementation. The role suits candidates who enjoy tackling complex business problems and bringing AI capabilities into operational environments.

Key responsibilities

• Data pipeline processing: Build batch and real‑time pipelines that integrate structured, unstructured, and multimodal data to support vector search, RAG, and AI applications.
• Data governance and quality control: Manage metadata, traceability, versioning, and quality rules; establish monitoring, exception handling, and rerun mechanisms to improve data reliability.
• Cross‑functional collaboration and standards: Work with AI/ML, architecture, and business teams to define data interfaces, performance, security, and deployment standards.
• Security, compliance, and ecosystem integration: Implement access control, de‑identification, and personal data compliance practices; connect data across systems and group entities while documenting usage, restrictions, and ownership.

Required profile

• Hands‑on data engineering experience with ability to independently design and debug maintainable ETL/ELT pipelines.
• Practical experience with at least one data processing, workflow orchestration, or streaming technology.
• Understanding of data models, APIs, file/object storage, data quality and performance optimization.
• Experience processing documents, text, images, audio or other unstructured/multimodal data.
• Ability to ensure long‑term maintainability of pipelines through clear documentation, testing and monitoring.

Required skills

• Python
• SQL
• ETL/ELT pipeline design, debugging and performance tuning
• Relational databases, file/object storage and data modeling
• Unstructured and multimodal data processing (documents, text, images, audio)
• Git version control, testing, documentation and monitoring
• Basic Linux operations

What success looks like

You will build data products with clear ownership, quality metrics and monitoring, enabling the team to access trusted data faster and launch new AI use cases more reliably. Your work will become a key foundation for the AI R&D division to continue scaling enterprise‑grade AI capabilities.