{"schemaVersion":"jobsearcher.job.v1","id":"212389777969e9208d0ebe5e","url":"https://jobsearcher.com/jobs/212389777969e9208d0ebe5e","canonicalUrl":"https://jobsearcher.com/jobs/212389777969e9208d0ebe5e","title":"Machine Learning Engineer-AI Data Platform","description":"MOBE helps people discover new ways to live healthier. We are the whole-person, cross-condition solution that goes further to deliver better health and lower overall costs through evidence-based individual health guidance and pharmacist-led medication management. We empower individuals to make meaningful changes that improve their health and overall well-being. Behind our innovative solutions are robust data analytics, digital application, and a uniquely human philosophy. With one-to-one connection and compassion, we uncover opportunities, overcome challenges, and motivate people to transform their lives.\r\nAt MOBE our team is our most significant asset. We cultivate a culture grounded in curiosity, innovation, and growth. We encourage new ideas, fresh solutions, and meaningful impact. We value a workforce made up of people with differences who are eager to learn from each other and grow personally and professionally. We extend this approach to our partners and communities, seeking to increase understanding and expand opportunities across all groups.\r\nYour role at MOBE\r\nWe are seeking a highly skilled AI Engineer to serve as a core builder of our AI Data Platform. This role sits at the intersection of machine learning engineering, data platform development, and business intelligence, with responsibility for designing and operating the infrastructure that powers AI-driven insights across the organization.\r\nYou will build intelligent data pipelines, production-grade ML systems, and AI-enabled features that translate complex data into actionable outcomes. This role is ideal for an engineer who enjoys working end-to-end from data ingestion and feature engineering to model deployment and downstream consumption in analytics and BI tools.\r\nApplicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.\r\nResponsibilities\r\nBuild AI-first data pipelines: Design, implement, and maintain scalable data pipelines that support model training, inference, and analytics use cases across the AI Data Platform.\r\nDeploy production ML systems: Develop, deploy, and monitor machine learning models using AWS SageMaker, ensuring reliability, observability, and performance in production environments.\r\nImplement Retrieval-Augmented Generation (RAG): Architect and maintain RAG-based systems that combine structured and unstructured data to power AI-driven insights and applications.\r\nOperationalize ML lifecycle management: Use MLflow for experiment tracking, model versioning, and lifecycle management to support reproducibility and continuous improvement.\r\nDesign feature infrastructure: Build and manage feature stores (e.g., Feast, Tecton, or SageMaker Feature Store) to ensure consistent, reusable features across training and inference.\r\nOrchestrate complex workflows: Create and manage Apache Airflow DAGs to orchestrate data transformations, model pipelines, and AI workflows with clear dependencies and monitoring.\r\nEnable analytics consumption: Partner with BI and analytics teams to ensure ML outputs integrate cleanly with our internal BI reporting hub.\r\nTranslate business questions into AI solutions: Collaborate with stakeholders to convert ambiguous business problems into measurable ML- and data-driven solutions.\r\nUphold data quality and governance: Ensure AI pipelines and models adhere to data governance, security, and quality standards, particularly when handling sensitive data.\r\nCollaborate cross-functionally: Work closely with Data Science, Analytics Engineering, Medical Economics, and DataOps to align AI platform capabilities with business priorities.\r\nQualifications\r\nRequired\r\nFive to Seven Years in the ML Engineering space.\r\nStrong proficiency in Python and SQL for data processing, modeling, and pipeline development.\r\nHands-on experience building and deploying machine learning models in production, including monitoring and performance management.\r\nExperience with AWS-based ML infrastructure, including SageMaker for training, deployment, and inference.\r\nPractical experience designing or operating RAG systems that integrate LLMs with enterprise data sources.\r\nExperience using MLflow (or equivalent) for experiment tracking, model registry, and lifecycle management.\r\nExperience with Apache Airflow for orchestration of data and ML pipelines.\r\nStrong foundation in data engineering concepts, including data modeling, versioning, and testing.\r\nAbility to partner with Med Econ and BI teams to ensure ML outputs are interpretable, trusted, and consumable.\r\nPreferred\r\nExperience with AWS Bedrock and/or Aider for LLM orchestration or AI-assisted development workflows.\r\nExperience with dbt for transformation modeling, testing, and documentation.\r\nFamiliarity with feature store architectures (Feast, Tecton, SageMaker Feature Store).\r\nExperience integrating ML outputs into BI tools such as Tableau, Looker, or QuickSight.\r\nExperience with CI/CD pipelines, Git-based workflows, and infrastructure-as-code practices.\r\nExposure to healthcare or regulated data environments is a plus but not required.\r\nNice to Have\r\nWorking knowledge of Docker and Kubernetes for scalable deployment of ML services.\r\nExperience implementing data observability, model drift detection, or AI governance tooling.\r\nExperience fine-tuning or adapting large language models for domain-specific use cases.\r\nValues\r\nPeople First. We show we care because we believe in the power of human connection.\r\nSpark Positivity. We each have the power to turn any challenge into something awesome.\r\nStay Curious. We relentlessly discover and embrace new ideas to keep moving forward.\r\nBenefits and Compensation\r\nWe offer a comprehensive benefits package.\r\nPaid time off\r\nMedical, dental, and vision insurance\r\nLife and disability insurance\r\n401(k) with company match\r\nAdditional benefits available to eligible employees\r\nThis is a hybrid position with the expectation of 3 days per week onsite in either our Minneapolis, MN or Reno, NV office.\r\nJ-18808-Ljbffr","company":"Mobe","rawCompany":"mobe","city":"Reno","state":"NV","isRemote":false,"isActive":false,"createdAt":"2026-04-12T01:54:38.065Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer-AI Data Platform","description":"MOBE helps people discover new ways to live healthier. We are the whole-person, cross-condition solution that goes further to deliver better health and lower overall costs through evidence-based individual health guidance and pharmacist-led medication management. We empower individuals to make meaningful changes that improve their health and overall well-being. Behind our innovative solutions are robust data analytics, digital application, and a uniquely human philosophy. With one-to-one connection and compassion, we uncover opportunities, overcome challenges, and motivate people to transform their lives.\r\nAt MOBE our team is our most significant asset. We cultivate a culture grounded in curiosity, innovation, and growth. We encourage new ideas, fresh solutions, and meaningful impact. We value a workforce made up of people with differences who are eager to learn from each other and grow personally and professionally. We extend this approach to our partners and communities, seeking to increase understanding and expand opportunities across all groups.\r\nYour role at MOBE\r\nWe are seeking a highly skilled AI Engineer to serve as a core builder of our AI Data Platform. This role sits at the intersection of machine learning engineering, data platform development, and business intelligence, with responsibility for designing and operating the infrastructure that powers AI-driven insights across the organization.\r\nYou will build intelligent data pipelines, production-grade ML systems, and AI-enabled features that translate complex data into actionable outcomes. This role is ideal for an engineer who enjoys working end-to-end from data ingestion and feature engineering to model deployment and downstream consumption in analytics and BI tools.\r\nApplicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.\r\nResponsibilities\r\nBuild AI-first data pipelines: Design, implement, and maintain scalable data pipelines that support model training, inference, and analytics use cases across the AI Data Platform.\r\nDeploy production ML systems: Develop, deploy, and monitor machine learning models using AWS SageMaker, ensuring reliability, observability, and performance in production environments.\r\nImplement Retrieval-Augmented Generation (RAG): Architect and maintain RAG-based systems that combine structured and unstructured data to power AI-driven insights and applications.\r\nOperationalize ML lifecycle management: Use MLflow for experiment tracking, model versioning, and lifecycle management to support reproducibility and continuous improvement.\r\nDesign feature infrastructure: Build and manage feature stores (e.g., Feast, Tecton, or SageMaker Feature Store) to ensure consistent, reusable features across training and inference.\r\nOrchestrate complex workflows: Create and manage Apache Airflow DAGs to orchestrate data transformations, model pipelines, and AI workflows with clear dependencies and monitoring.\r\nEnable analytics consumption: Partner with BI and analytics teams to ensure ML outputs integrate cleanly with our internal BI reporting hub.\r\nTranslate business questions into AI solutions: Collaborate with stakeholders to convert ambiguous business problems into measurable ML- and data-driven solutions.\r\nUphold data quality and governance: Ensure AI pipelines and models adhere to data governance, security, and quality standards, particularly when handling sensitive data.\r\nCollaborate cross-functionally: Work closely with Data Science, Analytics Engineering, Medical Economics, and DataOps to align AI platform capabilities with business priorities.\r\nQualifications\r\nRequired\r\nFive to Seven Years in the ML Engineering space.\r\nStrong proficiency in Python and SQL for data processing, modeling, and pipeline development.\r\nHands-on experience building and deploying machine learning models in production, including monitoring and performance management.\r\nExperience with AWS-based ML infrastructure, including SageMaker for training, deployment, and inference.\r\nPractical experience designing or operating RAG systems that integrate LLMs with enterprise data sources.\r\nExperience using MLflow (or equivalent) for experiment tracking, model registry, and lifecycle management.\r\nExperience with Apache Airflow for orchestration of data and ML pipelines.\r\nStrong foundation in data engineering concepts, including data modeling, versioning, and testing.\r\nAbility to partner with Med Econ and BI teams to ensure ML outputs are interpretable, trusted, and consumable.\r\nPreferred\r\nExperience with AWS Bedrock and/or Aider for LLM orchestration or AI-assisted development workflows.\r\nExperience with dbt for transformation modeling, testing, and documentation.\r\nFamiliarity with feature store architectures (Feast, Tecton, SageMaker Feature Store).\r\nExperience integrating ML outputs into BI tools such as Tableau, Looker, or QuickSight.\r\nExperience with CI/CD pipelines, Git-based workflows, and infrastructure-as-code practices.\r\nExposure to healthcare or regulated data environments is a plus but not required.\r\nNice to Have\r\nWorking knowledge of Docker and Kubernetes for scalable deployment of ML services.\r\nExperience implementing data observability, model drift detection, or AI governance tooling.\r\nExperience fine-tuning or adapting large language models for domain-specific use cases.\r\nValues\r\nPeople First. We show we care because we believe in the power of human connection.\r\nSpark Positivity. We each have the power to turn any challenge into something awesome.\r\nStay Curious. We relentlessly discover and embrace new ideas to keep moving forward.\r\nBenefits and Compensation\r\nWe offer a comprehensive benefits package.\r\nPaid time off\r\nMedical, dental, and vision insurance\r\nLife and disability insurance\r\n401(k) with company match\r\nAdditional benefits available to eligible employees\r\nThis is a hybrid position with the expectation of 3 days per week onsite in either our Minneapolis, MN or Reno, NV office.\r\nJ-18808-Ljbffr","datePosted":"2026-04-12T01:54:38.065Z","dateModified":"2026-04-12T01:54:38.065Z","hiringOrganization":{"@type":"Organization","name":"Mobe","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Reno","addressRegion":"NV","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"212389777969e9208d0ebe5e"},"url":"https://jobsearcher.com/jobs/212389777969e9208d0ebe5e"}}