Senior Data Engineer — AI-Ready Pipelines & RAG Systems

AVENSYS CONSULTING PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Benefits offered by this job

Competitive base salary

Career progression

Fun collaborative environment

Job summary

Avensys Consulting Pte. Ltd. in Singapore is seeking a Data Engineer to design and operate data pipelines for AI-ready datasets.

You will build both batch and streaming pipelines using Airflow, Kafka, and related technologies, ensuring high data quality and governance across engagements. The role requires extensive experience in data engineering, AI/ML data pipelines, and collaboration with AI Engineers to define data requirements and readiness for model/RAG workloads.

Qualifications

• 10+ years in data engineering with production pipelines.
• Strong SQL and Python/Scala/Java; batch and streaming: Airflow, Spark, Kafka.
• Experience building data pipelines for AI/ML or RAG use cases, including embeddings and vector indexing.
• Knowledge of data governance, PII handling, and regulated environments (PDPA).

Responsibilities

• Design ingestion, cleaning, and transformation pipelines for AI-ready datasets.
• Build batch and streaming pipelines (Airflow/Prefect/Kafka) to feed AI systems.
• Own data quality, validation, and lineage upstream of models and RAG pipelines.
• Collaborate with AI Engineers and AI Architects on data requirements and architecture.

Skills

Data engineering

SQL

Python

Java/Scala

Airflow

Kafka

Spark

AI/ML data pipelines

Tools

pgvector

Pinecone

Weaviate

OpenSearch

Azure Open Search

Presidio

Job description

Avensys Consulting Pte. Ltd. in Singapore is seeking a Data Engineer to design and operate data pipelines for AI-ready datasets.

You will build both batch and streaming pipelines using Airflow, Kafka, and related technologies, ensuring high data quality and governance across engagements. The role requires extensive experience in data engineering, AI/ML data pipelines, and collaboration with AI Engineers to define data requirements and readiness for model/RAG workloads.