AI Engineer - Manufacturing Data

Bausch+Lomb Companies Inc.•Rochester, NY

4d•$81,600 - $110,400•Onsite

About The Position

This role sits at the intersection of the plant floor and the data platform. We are building our data and AI capability from the ground up — replacing fragmented systems, manual CSV exports, and information gaps with reliable pipelines, clean data, and intelligent tools. The first mission is foundational: build the infrastructure that gives operations, quality, and maintenance teams the visibility they need to make faster, better decisions. From there, this role will lead the application of agentic AI and ML platforms to automate workflows, surface insights, and scale impact across the plant. Success is measured by solutions shipped, adopted, and making a measurable difference on the floor.

Requirements

Bachelor’s degree in Engineering, Computer Science, Information Systems, Industrial Engineering, or related field required.
3+ years of hands-on relevant experience strongly preferred; experience with manufacturing data systems, including ERP, MES, or QMS platforms.
Demonstrated history of delivering working solutions - dashboards, automations, integrations, or AI tools actively used in production.
Manufacturing fluency — genuine understanding of manufacturing processes, quality systems, and operational workflows; able to translate floor requirements into technical solutions.
Power BI, Power Apps & Power Automate — experienced building workflow automation and plant-facing applications.
Data pipeline & integration — design data collection structures, build integrations, and replace manual processes with reliable automated flows.
Agentic AI — hands-on experience building AI agents or Copilot solutions; not just using LLMs but building with them.
Systems integration — connecting ERP, MES, QMS, and shop floor systems into unified reporting environments.
Regulated environment — comfortable within GxP and/or ISO frameworks where solutions must be validated and documented.
Project ownership — able to manage projects independently, set priorities, and deliver without waiting for complete direction.
Communication — equally credible with a maintenance technician on the floor and with plant leadership.

Nice To Haves

SQL — writing and modifying queries for extraction, transformation, and validation.
Azure AI services — Azure OpenAI, AI Search, Document Intelligence, or Data Factory.
Python — basic scripting for data manipulation, pipeline automation, or API integration.
Lean / Six Sigma — Green Belt or Black Belt.
Shop floor data systems — historian platforms, SCADA, or similar real-time sources.
Life sciences / pharmaceutical manufacturing — familiarity with FDA-regulated environments and data integrity requirements

Responsibilities

Assess current plant data infrastructure — identify where data is missing, poorly structured, or trapped in manual processes.
Design and build automated pipelines that replace manual CSV exports with sustainable, auditable data flows.
Integrate data from ERP, MES, QMS, and shop floor systems into unified reporting environments.
Ensure all pipelines and collection methods meet GxP and ISO documentation and validation requirements.
Build dashboards and reports that translate manufacturing data into actionable insights for operators, supervisors, and leadership.
Develop Power Apps and Power Automate workflows that reduce manual effort for quality, maintenance, and operations teams.
Apply manufacturing knowledge — OEE, yield, cycle time, process variation — to ensure analytics are practically useful.
Build AI-powered agents and workflows using Microsoft Copilot Studio or Claude, starting with shift handover, production reporting, and maintenance tracking.
Integrate Azure AI services (Azure OpenAI, AI Search, Document Intelligence) to create intelligent tools that reason, retrieve, and respond.
Ensure all AI solutions are validated, documented, and auditable in a GxP/ISO environment.
Work directly with plant supervisors, maintenance leads, quality engineers, and operations personnel to gather requirements and validate solutions.
Champion data-driven decision-making by building trust in data quality through consistent, reliable delivery.
Collaborate with IT Infrastructure Operations on hosting, access controls, data architecture, and compliance.
Lead projects for implementing or improving QMS, analytics, and automation software from requirements through sustainment.