Back
Tier-1 employer, metro location, mid-level generalist ML role, and broad GenAI skills increase competition.
Core ML/AI and MLOps skills are transferable, but healthcare regulatory experience increases domain specificity.
Explicit 3–6 year band plus specific MLOps/GenAI and healthcare compliance expectations produce moderate rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy production-grade AI and data solutions including predictive failure models, GenAI assistants, and analytics for MRI service lifecycle.
Build and maintain robust data pipelines and operationalise ML models, LLMs, and agentic AI workflows for global deployment in healthcare service tools.
Take AI prototypes to MVP and productization with best practices like MLOps/LLMOps, CI/CD, monitoring, and compliance in a regulated medical device environment.
Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field.
3-6 years experience in Data Engineering, AI Engineering, Machine Learning Engineering or similar roles (4+ years with vocational qualification).
Experience with Python programming, data engineering, ML frameworks, cloud platforms (Azure preferred), and AI deployment best practices.
Role requires working onsite at company facilities, with a global distributed team across India, Europe, and North America.
Experienced in taking Generative AI and agentic AI applications from POC to scalable production in enterprise environments.
Familiarity with healthcare or regulated industries, particularly medical device service operations and remote diagnostics data.
Capable of collaborating across distributed global teams and translating complex service domain problems into AI-driven engineering solutions.