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Strong employer brand, mid-level generalist AI role, metro location, and broad skillset requirements increase competition.
Core ML/AI and MLOps skills transfer across industries, though MedTech/regulatory experience is a moderate preference.
Explicit years bands, mandatory ML/AI and MLOps skills, and regulated-industry constraints make shortlisting stringent.
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Design, develop, and deploy production-grade AI solutions including predictive failure models, GenAI assistants, and analytics across MRI service lifecycle.
Build and maintain robust data pipelines and operationalize ML models and AI workflows integrated into service tools used globally.
Implement AI engineering best practices (MLOps/LLMOps, CI/CD, monitoring) in a regulated healthcare environment, ensuring model performance and compliance.
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 related fields with Bachelor's; minimum 4 years with vocational equivalent qualification.
Experience with Azure AI Services, Azure Machine Learning, or Amazon Bedrock for enterprise AI application deployment.
Role is office-based requiring presence at company facilities at least 3 days per week (India location).
Experienced in taking Generative AI or agentic AI applications from POC to production in enterprise environments, preferably in healthcare or regulated industries.
Proficient in Python programming, data engineering, ML frameworks, and cloud platforms, with skills in MLOps and Responsible AI governance.
Able to work effectively in a globally distributed, remote-first team spanning India, Europe, and North America, collaborating with service domain experts.