Data Engineer (AI Infrastructure)
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Summary
Build and maintain large-scale data pipelines that support AI model training and evaluation at a fast-growing AI technology company in Singapore. Day-to-day work centers on Python, SQL, and distributed processing frameworks like Spark or Ray, plus data cleaning, transformation, and pipeline optimization.
Data Engineer (AI Infrastructure)
About the Role
Our client is a fast-growing AI technology company looking for a
Data Engineer
to build and optimize large-scale data pipelines that support AI model development. You will work closely with AI, Data, and Engineering teams to process large datasets, improve data quality, and build reliable data infrastructure. Responsibilities
Build and maintain scalable data processing pipelines. Develop tools for data cleaning, transformation, filtering, and data preparation. Optimize data processing performance and improve pipeline efficiency. Work with large datasets to support AI model training and evaluation. Collaborate with AI and engineering teams to ensure reliable and high-quality data. Monitor pipeline performance and troubleshoot production issues. Improve data quality, reliability, and automation across the platform. Requirements
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related discipline. 2-6 years
of experience in Data Engineering, Big Data, or Software Engineering. Strong programming skills in
Python . Experience with SQL and relational databases. Experience with at least one distributed data processing framework such as
Spark
or
Ray . Familiarity with data pipelines and ETL workflows. Good problem-solving skills and ability to work with large datasets. Strong communication and teamwork skills. Nice to Have
Experience with
Kafka ,
Flink ,
Iceberg , or
Delta Lake . Experience with cloud platforms (AWS, Azure, GCP). Experience with GPU computing or AI/ML infrastructure. Familiarity with Docker, Kubernetes, or CI/CD. Experience supporting AI or machine learning projects. Knowledge of LLMs or generative AI is a plus.
Regret to inform that only shortlisted candidates will be notified. www.dadaconsultants.com EA Registration Number: R1878287 Business Registration Number: 201735941W.
About the Role
Our client is a fast-growing AI technology company looking for a
Data Engineer
to build and optimize large-scale data pipelines that support AI model development. You will work closely with AI, Data, and Engineering teams to process large datasets, improve data quality, and build reliable data infrastructure. Responsibilities
Build and maintain scalable data processing pipelines. Develop tools for data cleaning, transformation, filtering, and data preparation. Optimize data processing performance and improve pipeline efficiency. Work with large datasets to support AI model training and evaluation. Collaborate with AI and engineering teams to ensure reliable and high-quality data. Monitor pipeline performance and troubleshoot production issues. Improve data quality, reliability, and automation across the platform. Requirements
Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related discipline. 2-6 years
of experience in Data Engineering, Big Data, or Software Engineering. Strong programming skills in
Python . Experience with SQL and relational databases. Experience with at least one distributed data processing framework such as
Spark
or
Ray . Familiarity with data pipelines and ETL workflows. Good problem-solving skills and ability to work with large datasets. Strong communication and teamwork skills. Nice to Have
Experience with
Kafka ,
Flink ,
Iceberg , or
Delta Lake . Experience with cloud platforms (AWS, Azure, GCP). Experience with GPU computing or AI/ML infrastructure. Familiarity with Docker, Kubernetes, or CI/CD. Experience supporting AI or machine learning projects. Knowledge of LLMs or generative AI is a plus.
Regret to inform that only shortlisted candidates will be notified. www.dadaconsultants.com EA Registration Number: R1878287 Business Registration Number: 201735941W.