AI Platform Engineer - Production & Integrations

Boston Scientific Gruppe

Arden Hills (MN)

On-site

USD 78,000 - 149,000

Full time

6 days ago

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Job summary

Boston Scientific AIS R&D is seeking an AI Solutions Delivery engineer to join the AI Transformation core team and scale the AI-assisted engineering platform. You will configure tools, build integrations and enable pilot workflows, working with the AI Solutions Architect on design standards and with the AI Solutions Lead on turning validated pilots into production-ready capabilities.

The role is hands-on and focuses on reliability, access management and issue resolution as production workflows

Qualifications

Bachelor's degree in computer science, engineering or a related technical field.
Minimum of 3 years' experience building, deploying or operating production software or platform services.
Experience integrating enterprise systems and tools (e.g., PLM/Windchill, Jira, GitLab, CI/CD platforms, Snowflake) into a shared platform or service.
Experience with cloud infrastructure (AWS, Azure or similar), including deployment, monitoring and access management.
Experience troubleshooting production issues, including monitoring, alerting and incident response.

Responsibilities

Implement and sustain the AI-assisted engineering platform, including tool configuration and workflow enablement during pilots.
Build and maintain integrations between the AI platform and enterprise engineering systems, including Windchill, Jira, GitLab, AWS, Azure and other tools.
Manage solution reliability, monitoring and issue resolution as workflows move from pilot to production.
Manage user access, permissions and onboarding for platform users across AIS R&D functions.
Coordinate with Enterprise AI and IT teams on platform alignment, tool governance and enterprise non-negotiables.
Partner with the AI Solutions Architect to implement architecture direction, design standards and integration patterns.
Partner with the AI Solutions Lead to operationalize validated pilot designs into supported, production-ready capabilities.
Operationalize reliability, access management and issue resolution practices at scale as the number of in-scope workflows grows.
Contribute to a culture of ownership, experimentation and operational excellence.