AI Driven Automation Solutions Architect (Pharma)
Orise, Greenville, SC, United States
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.
Key Responsibilities
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
Additional Information
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 SageMaker Pipelines for ML workflow orchestration
Model versioning and governance using Amazon SageMaker 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
Preferred Qualifications
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
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Key Responsibilities
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
Additional Information
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 SageMaker Pipelines for ML workflow orchestration
Model versioning and governance using Amazon SageMaker 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
Preferred Qualifications
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
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