Data Engineer (AI Infrastructure)

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.