Data Analytics & AI Engineer

Position: Data Analytics & AI Engineer

Location: Cork, Ireland (Hybrid).

Contract position

Job Description :

We are looking for a self-motivated Data Analytics and AI Engineer to join our Manufacturing Data & AI team. This role sits at the intersection of data engineering, analytics, and applied AI: you will build and operate reliable data pipelines on Azure Databricks/AWS, deliver analytics and AI solutions that turn manufacturing data into decisions, and design AI solutions using Agents and Large Language Models (LLMs) that can plan, reason, and act on real manufacturing problems. You will work closely with manufacturing, quality, and engineering stakeholders to translate business needs into production-grade data, analytics, and AI solutions, taking ownership from problem framing through delivery and support.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).
• Hands-on experience with Azure Databricks (or equivalent Spark-based platform) building and operating data pipelines and analytics solutions.
• Practical experience building analytics solutions (dashboards, reporting, data models) that stakeholders use for decision-making.
• Practical experience developing AI solutions using Agents and/or LLMs (e.g., prompt engineering, RAG, agent frameworks, tool/function calling).
• Proficiency in Python and SQL; familiarity with modern data engineering tooling (e.g., Delta Lake, Airflow/Databricks Workflows, Git-based CI/CD).
• Strong self-starter with a track record of driving projects to completion with minimal supervision.
• Clear communication skills, able to explain technical solutions to non-technical manufacturing stakeholders.

Preferred Qualifications

• Brief exposure to or working knowledge of GMP (Good Manufacturing Practice) or other regulated-industry data environments.
• Experience in a manufacturing, pharmaceutical, or life sciences business context.
• Experience with cloud platforms beyond Azure (AWS/GCP), MLOps/LLMOps practices, or vector databases.
• Exposure to data governance, data cataloging, or data security frameworks in an enterprise setting.