Data Engineer (AI)

TL;DR

Data Engineer (AI/ML): Designing and developing scalable data pipelines and applied AI solutions for manufacturing and operations with an accent on data quality and ML model deployment. Focus on transforming operational data into actionable insights through anomaly detection and predictive analytics.

Location: Alajuela, CR

Company

Global test and automation specialists providing manufacturing automation solutions across various industries.

What you will do

• Design, build, and maintain scalable data pipelines to process manufacturing, test, and operational data.
• Establish data schemas, logging practices, and quality processes to enable reliable analytics and AI applications.
• Develop analytics-ready datasets and reporting layers using SQL and BI platforms like Power BI.
• Contribute to the development of pipelines for training, evaluating, and deploying machine learning models.
• Apply machine learning techniques for use cases such as anomaly detection, pattern recognition, and predictive insights.
• Collaborate with Operations and Engineering teams to translate operational challenges into scalable data solutions.

Requirements

• B.S. in Computer Science, Computer Engineering, or a closely related field.
• 3+ years of experience delivering production-quality data solutions and/or applied ML systems.
• Proficiency in Python for data processing, analytics, and AI workflows.
• Strong SQL skills for querying, transforming, and analyzing large datasets.
• Experience with programming languages such as C#, Java, or Scala.
• Knowledge of software engineering fundamentals, including version control and testing practices.

Nice to have

• Experience working with manufacturing, test, or operational data.
• Familiarity with modern data platforms such as Microsoft Azure and/or Snowflake.
• Experience supporting BI or analytics platforms like Power BI.
• Exposure to production ML systems, including deployment, monitoring, and MLOps concepts.
• Experience with advanced ML approaches such as graph-based modeling, optimization, or reinforcement learning.