AI Data Engineer

Role Overview

An AI Data Engineer is responsible for designing, building, and managing data infrastructure that supports AI and Machine Learning systems. This role focuses on creating scalable data pipelines, preparing high-quality datasets, and enabling efficient model training and deployment.

Key Responsibilities

• Build and maintain data pipelines for AI/ML workflows
• Collect, clean, and preprocess structured and unstructured data
• Design and manage data lakes and data warehouses
• Develop ETL/ELT processes for large-scale data processing
• Optimize data storage and retrieval for AI model performance
• Integrate data from multiple sources (APIs, databases, streaming systems)
• Collaborate with data scientists and AI engineers to provide training datasets
• Implement data validation, quality checks, and governance policies
• Work with real-time and batch data processing systems
• Monitor and troubleshoot data pipeline performance issues

Required Skills

• Strong knowledge of SQL for data querying and transformation
• Proficiency in Python or Java for data processing
• Understanding of data engineering concepts (ETL, data pipelines)
• Familiarity with databases (MySQL, PostgreSQL, MongoDB)
• Knowledge of data structures and algorithms
• Understanding of data preprocessing techniques for ML
• Problem-solving and analytical thinking

Preferred Skills

• Experience with big data tools (Apache Spark, Hadoop)
• Familiarity with AI/ML workflows and data requirements
• Knowledge of data warehouse tools (Amazon Redshift, Google BigQuery, Snowflake)
• Experience with streaming tools (Kafka, Flink)
• Understanding of MLOps practices
• Experience with cloud platforms like Amazon Web Services, Microsoft Azure, or Google Cloud Platform
• Familiarity with workflow orchestration tools (Apache Airflow)
• Basic knowledge of Docker and Kubernetes

Tools & Technologies

• Languages: Python, Java, SQL
• Big Data: Apache Spark, Hadoop
• Databases: MySQL, PostgreSQL, MongoDB
• Data Warehouses: Redshift, BigQuery, Snowflake
• Streaming: Kafka, Flink
• Orchestration: Apache Airflow
• Platforms: AWS, Azure, Google Cloud Platform