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Tier-1 brand, metro location, mid-level generalist ML role, and broad skills amplify competition.
Healthcare-focused data, regulated AI governance, and deployable medical imaging constraints limit cross-industry transferability.
Explicit years, required ML/MLOps skills, and healthcare compliance needs raise screening strictness.
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Design, develop, and deploy production-grade AI and data solutions including predictive failure models, GenAI tools, and analytics for MRI service lifecycle.
Build and maintain scalable, secure data pipelines and operationalize ML models and AI workflows for global field and remote engineers.
Manage AI model lifecycle: from POC to productization, including MLOps/LLMOps, monitoring, retraining, and compliance in a regulated healthcare environment.
Bachelor's or Master's Degree in Computer Science, AI, Data Science, IT, Software Engineering, Statistics, Mathematics, or related.
3-6 years of experience in Data Engineering, AI Engineering, Machine Learning Engineering, or Software Engineering (or 4+ years with vocational/equivalent qualification).
Experience with programming (Python preferred), ETL/ELT, ML model deployment, cloud platforms (Azure preferred), and MLOps practices.
This is an office-based role; onsite presence required at company facilities.
Experienced in end-to-end AI solution development with ability to convert POCs into enterprise-grade products in regulated environments.
Familiar with generative AI and agentic AI applications, cloud-based AI services (Azure/AWS), and responsible AI governance.
Capable of working across global teams to translate service domain requirements into scalable, monitored, and compliant AI solutions impacting MRI system uptime and service efficiency.