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Metro location, mid-level AI role, and broad GenAI skill requirements increase applicant competition.
Core GenAI and ML skills are transferable, though healthcare insurance experience is preferred.
Explicit 3+ years plus mandatory GenAI, Kubernetes, Kubeflow, and ML framework requirements raise strictness.
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Design and optimize data engineering systems for large-scale batch and real-time processing in healthcare AI applications.
Develop and enhance retrieval-augmented generation (RAG) systems leveraging latest LLM technologies for efficient, relevant medical and insurance data search.
Build, optimize, and evaluate scalable machine learning and verifiable ML models that support analysis and decision-making in complex healthcare and insurance data environments.
Bachelor's degree in Computer Science (focus on ML/AI), Engineering, Statistics, or related field.
Minimum 3 years of industry experience, including recent Gen AI technologies.
Proficient with Python, multiple ML frameworks, containerization, Kubernetes, and cloud-native ML tools like Kubeflow.
Experience with CI/CD pipelines and automated testing.
Experienced in developing ML systems in healthcare or insurance domains with emphasis on data security and governance.
Capable of building production-grade, scalable AI systems with strong operational awareness (latency, cost, evaluation metrics).
Familiarity with clinical coding standards (ICD-10, CPT, HCPCS) and US healthcare insurance environment is a strong advantage.