Machine Learning/AI Engineer Lead in Santa Rosa
Role details
Job location
Tech stack
Requirements
We are seeking a hands-on Machine Learning / AI Engineer to lead the adoption, governance, and productionization of ML and AI solutions within a leading Dental Insurance organization. This role will serve as the technical advisor and mentor for data scientists and ML practitioners, establishing best practices, scalable architectures, and operational frameworks for AI/ML solutions. The ideal candidate has strong experience with Snowflake, Azure Machine Learning, MLOps, and enterprise AI platforms., n
7+ years of experience in Data Science, Machine Learning, or AI Engineering.
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Strong hands-on experience with Azure Machine Learning and MLOps.
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Experience working with Snowflake, including Snowpark, ML capabilities, and AI features.
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Proficiency in Python and ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost.
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Experience deploying and monitoring ML models in production environments.
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Strong understanding of feature engineering, model governance, and model lifecycle management.
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Excellent communication and stakeholder management skills., n
Experience in Healthcare or Insurance domains (Dental Insurance ).
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Experience with Generative AI, LLMs, RAG architectures, and AI governance.
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Familiarity with Azure OpenAI, Snowflake Cortex, and enterprise AI platforms.
Benefits & conditions
Define and implement enterprise best practices for Machine Learning, Generative AI, and MLOps.
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Partner with business, product, data engineering, and analytics teams to identify and prioritize AI/ML use cases.
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Guide data scientists and analysts in developing production-ready ML solutions.
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Design, build, and operationalize ML models and AI solutions using Snowflake and Azure ML.
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Establish model governance, monitoring, versioning, explainability, and model lifecycle management processes.
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Collaborate with Data Engineering teams to build scalable feature engineering and data pipelines.
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Evaluate and recommend AI/ML tools, frameworks, and architectural patterns.
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Support deployment and operationalization of predictive analytics, NLP, GenAI, and intelligent automation use cases.
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Ensure compliance with security, privacy, and regulatory requirements in a healthcare/insurance environment.
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