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Mid-level ML/AI role with common experience band and metro visibility increases applicant density.
ML skills transfer broadly but healthcare clinical coding preference reduces cross-industry fit.
Explicit 3+ years plus required Kubernetes, Kubeflow, cloud-native ML, CI/CD, and ML frameworks raises filter strictness.
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Design and optimize systems for processing large-scale batch and real-time healthcare data.
Develop and improve retrieval-augmented generation (RAG) systems using latest large language model (LLM) technologies to enhance search relevance and efficiency.
Build scalable, production-ready ML models and verifiable systems focused on analyzing complex medical and insurance data with high safety and quality standards.
Bachelor's degree in Computer Science, Engineering, Statistics, or related field with focus on ML/AI.
Minimum 3 years industry experience including hands-on work with recent Generative AI technologies.
Proficiency in Python, ML frameworks, containerization (Kubernetes), cloud-native ML (Kubeflow), CI/CD, and automated testing.
Work Experience Required: At least 3 years in relevant industry roles.
Experienced ML engineer with strong background in generative AI models and retrieval-augmented generation systems.
Skilled in building scalable, secure, and production-grade AI systems in data-intensive environments, preferably healthcare or insurance context.
Familiarity with healthcare data standards, medical coding, or insurance domain is a strong advantage but not mandatory.