AI Driven Automation Solutions Architect; Pharma

Job Description

We are seeking an Engineer to provide on-site engineering and application support for pharmaceutical manufacturing operations in New Albany, Ohio. This role supports Line Clearance and other DISO applications, while also contributing to the operation and support of production-grade AWS-based machine learning platforms used in regulated manufacturing environments.

• Provide on-site engineering and application support for Line Clearance systems and other DISO applications
• Support day-to-day operations, troubleshooting, and incident resolution for production and quality-critical systems
• Collaborate with manufacturing, quality, IT, and digital teams to ensure system availability, performance, and compliance with GMP requirements
• Support AWS-based ML platforms used in manufacturing and quality use cases, including monitoring, operational support, and controlled deployments
• Assist with system configuration, testing, validation, and documentation activities in accordance with site and regulatory standards
• Participate in change management activities, including impact assessments, implementation support, and post-change verification
• Support investigations and root cause analysis related to application or data issues
• Coordinate with global engineering, IT, and data platform teams as needed
• Maintain clear documentation and communication aligned with site procedures and operational best practices

• Bachelor’s degree in Engineering, Computer Science, Information Systems, or a related technical discipline
• Experience supporting manufacturing or quality systems in regulated environments (pharmaceutical, biotech, or life sciences)
• Experience with Line Clearance processes and Machine Learning applications
• Strong troubleshooting, analytical, and problem-solving skills
• Ability to work effectively in an on-site, cross-functional environment
• Strong written and verbal communication skills
• Experience supporting production-grade AWS-based machine learning platforms, including:
• AWS Sage Maker Pipelines for ML workflow orchestration
• Model versioning and governance using Amazon Sage Maker Model Registry or equivalent tools
• Deployment and operation of ML solutions across multi-account AWS environments (development, validation, and production)
• Understanding of MLOps best practices, including model lifecycle management, CI/CD for ML, monitoring, and controlled promotion across environments
• Familiarity with AWS Well-Architected Framework principles, particularly security, reliability, and operational excellence, as applied to ML platforms

• Experience working in GMP-regulated manufacturing environments
• Exposure to system validation, change control, and regulated documentation practices
• Experience supporting cloud-based data or analytics platforms in life sciences
• Knowledge of pharmaceutical manufacturing workflows and quality systems
• Prior experience supporting Amgen systems or similar enterprise manufacturing environments