jobs in Basil Technologies

全职 Data AI Engineer 工作, 薪水, Basil Technologies Johor 公司招聘中 - Ricebowl

Data AI Engineer

Basil Technologies

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工作地点

  • Johor Bahru Johor Malaysia

职位描述

岗位职责

We are looking for a Data Engineer to own the data pipelines, storage architecture, and AI-enablement layer for a media monitoring platform. This role will focus on building reliable data foundations for large-scale ingestion, processing, enrichment, and serving of multilingual media content for NLP and machine learning use cases.

Core Responsibilities

  • Design and maintain scalable batch and streaming pipelines for news, social media, web, and broadcast data ingestion.
  • Build ETL/ELT processes to clean, normalize, deduplicate, enrich, and structure unstructured media content.
  • Prepare datasets, labels, and feature-ready data for AI/ML model training, fine-tuning, and evaluation.
  • Support NLP workflows such as entity extraction, topic classification, sentiment analysis, clustering, summarisation, and alerting.
  • Ensure data quality, schema consistency, lineage, observability, and fault tolerance across the platform.
  • Optimize storage, compute, and query performance across the data stack.
  • Implement governance, access control, and auditability for sensitive or regulated content.
  • Work closely with the full stack developer to align backend data services with product requirements and API consumption patterns.

Required Skills

  • Strong experience in data engineering, pipeline orchestration, and distributed data processing.
  • Proficiency in Python and SQL.
  • Experience with cloud data platforms, object storage, warehouses, workflow orchestration, and message queues.
  • Familiarity with unstructured text data, NLP workflows, and ML data preparation.
  • Understanding of data modeling, system reliability, monitoring, and performance tuning.

Preferred Experience

  • Background in media monitoring, social listening, content intelligence, or news analytics.
  • Experience with multilingual datasets and text-heavy pipelines.
  • Exposure to LLM-based systems, vector search, or retrieval pipelines.
  • Familiarity with secure deployment environments and data governance practices.

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