Data Analytics & AI Engineer

Role Overview

Join the Manufacturing Data & AI team to transform manufacturing data into actionable insights. You will develop reliable data pipelines, create interactive Power BI dashboards, and design AI agent and large language model (LLM) solutions that address practical manufacturing challenges. Collaborate directly with teams across manufacturing, quality, and engineering from initial problem definition to final delivery.

Key Responsibilities

• Develop and maintain scalable batch and streaming data pipelines using Azure Databricks and AWS platforms, ensuring data quality, implementing monitoring tools, and managing continuous integration/deployment (CI/CD).
• Create dashboards, semantic layers, and self-service data products that provide meaningful business insights.
• Design, deploy, and operationalize AI applications involving agents and LLMs, including retrieval-augmented generation (RAG), tool integrations, and multi-step workflows, all integrated with enterprise datasets and systems.
• Lead projects autonomously from requirement gathering through deployment, adhering to Good Manufacturing Practice (GMP) standards for data integrity and traceability.

Required Qualifications and Skills

• Bachelor’s degree or equivalent experience in Computer Science, Engineering, Data Science, or a related field.
• Proficient with Azure Databricks or comparable Apache Spark environments.
• Experience delivering analytical tools and solutions that influence business decisions.
• Hands-on experience in constructing AI solutions incorporating agents and/or large language models.
• Strong programming skills in Python and SQL, familiar with Delta Lake, Airflow or Databricks Workflows, and Git-driven CI/CD pipelines.
• Self-motivated individual with excellent communication skills to engage effectively with non-technical stakeholders.

Preferred Experience

• Background in regulated industries such as manufacturing, pharmaceuticals, or life sciences, with knowledge of GMP protocols.
• Exposure to AWS or Google Cloud Platform (GCP), MLOps or LLMOps frameworks, and vector database technologies.
• Understanding of data governance and data cataloguing practices.