Lead AI Data Engineer: GenAI Pipelines & RAG

Randstad Singapore; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

5 days in office

Job summary

Randstad Singapore is hiring multiple headcounts for Senior and Lead level data engineering roles. You will design, build, and maintain scalable batch and streaming data pipelines to support analytics and ML initiatives.

You will transform, cleanse, and validate data using standard frameworks and work on GenAI-related architectures such as RAG, vector databases, and embeddings for enterprise applications.

Qualifications

• Bachelor's degree in Computer Science or Software Engineering required.
• ≥5 years hands-on data engineering, cloud analytics, or data platform development.
• Strong Python and SQL, plus experience with PySpark/Apache Spark.
• Proven track record building streaming and batch pipelines with Databricks, Delta Lake, and Kafka.
• Experience with RAG stacks, vector databases, embeddings for GenAI; knowledge of CI/CD, Git, and cloud observability.

Responsibilities

• Design, build, and maintain scalable batch and streaming data pipelines to support analytics and machine learning initiatives
• Perform data transformation, cleansing, and validation using standard engineering frameworks
• Implement and operationalize knowledge bases, vector search, and Retrieval-Augmented Generation (RAG) architectures for enterprise GenAI applications

Skills

Python

SQL

Apache Spark

Databricks

Kafka

Data pipelines

GenAI knowledge

CI/CD

Education

Bachelor's degree in Computer Science / Software Engineering

Tools

Delta Lake

Vector databases

Kafka

Job description

Randstad Singapore is hiring multiple headcounts for Senior and Lead level data engineering roles. You will design, build, and maintain scalable batch and streaming data pipelines to support analytics and ML initiatives.

You will transform, cleanse, and validate data using standard frameworks and work on GenAI-related architectures such as RAG, vector databases, and embeddings for enterprise applications.