Responsibilities:

• Design, build, and maintain AI data infrastructure for scalability, reliability, and performance.
• Collaborate with data scientists.
• Monitor and troubleshoot AI pipelines and processing workflows to identify and resolve performance and reliability issues.

Requirements:

• Bachelor's degree or higher in Computer Science, Computer Engineering, or a related field.
• 5+ years of overall experience in data engineering.
• Proven experience in deploying data engineering pipelines and machine learning models into production.
• Proficiency in scripting Python, Scala, or Java for AI data processing and ETL.
• Strong knowledge of SQL and NoSQL databases, including systems like HBase.
• Experience with distributed computing frameworks like Spark and Hadoop for large-scale data processing.
• Familiarity with containerization platforms like Docker and Kubernetes for deploying and managing AI data infrastructure.
• Familiarity with workflow management tools like Airflow or Kubeflow for designing and scheduling AI pipelines.
• Familiarity with monitoring systems like Grafana, Prometheus, or ELK stack is a plus.
• Ability to monitor and troubleshoot AI pipelines and processing workflows, identifying and resolving performance and reliability issues.