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Mid-level ML role, metro location, generalist title, and broad skill requirements increase applicant competition.
Core ML and MLOps skills transfer across industries, but insurance back-office context adds moderate domain specificity.
Explicit 4–6 years plus mandatory MLOps, Docker/Kubernetes, CI/CD, and cloud requirements create stringent filters.
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Develop and deploy AI/ML and Generative AI solutions with a focus on enterprise use cases.
Build and maintain end-to-end ML pipelines including training, production deployment, monitoring, and performance tracking.
Collaborate cross-functionally to optimize scalable AI systems using tools like APIs, vector databases, and RAG-based applications.
4-6 years of hands-on experience in building and deploying AI/ML solutions.
Strong practical knowledge in Machine Learning, Deep Learning, and Generative AI.
Proficiency in Python, SQL, API development, and ML frameworks.
Experience with MLOps, CI/CD pipelines, Docker, Kubernetes, MLflow; familiarity with Azure Cloud Stack is a plus.
Experienced in managing full AI/ML model lifecycle including deployment and monitoring at scale.
Skilled in operating within cloud-native architectures, specifically with container orchestration and modern MLOps tools.
Capable of collaborating across teams to integrate AI/ML solutions into complex back-office or enterprise platforms in the insurance domain.