Contribute to the design and evolution of cloud and ML architecture, helping identify scalable andreusable approaches across models, clients, and use cases.
Build and improve deployment automation, CI/CD, versioning, testing, environment management,reproducibility, and production release processes.
Implement monitoring and observability across ML pipelines and production solutions, includingfailures, performance, reliability, data quality, and resource utilization.
Optimize application code, queries, data processing, pipelines, and cloud resources for performance,scalability, maintainability, and cost.
Support the productionization of generative AI and agentic capabilities, including integration withenterprise data, APIs, applications, and operational workflows.
Design and maintain the data structures and processing workflows required to support modeldevelopment, scoring, monitoring, and production applications.
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Troubleshoot issues across applications, cloud services, data pipelines, and ML workflows andcollaborate with data scientists and engineers to identify durable solutions.
Help establish reusable engineering patterns and standards for moving AI/ML capabilities throughdevelopment, testing, production, monitoring, and ongoing maintenance.
Qualifications
3+ years of relevant experience in MLOps, software engineering, cloud engineering, machinelearning engineering, or a related technical field.
Strong software engineering skills and experience developing maintainable, production-qualityapplications, services, or technical solutions.
Strong Python skills and experience working in Python-based data science or machine learningenvironments.
Experience building and operating cloud-based data and machine learning pipelines, including dataprocessing, orchestration, automation, and integration with production systems.
Experience supporting the deployment and operation of machine learning models or otheranalytical solutions in production.
Strong SQL skills and experience working with large analytical datasets and cloud-based dataenvironments.
Experience with modern software engineering practices including source control, testing, codereview, CI/CD, dependency management, and multiple deployment environments.
Understanding of the machine learning lifecycle and experience working closely with data scientists.
Experience developing or integrating APIs, services, applications, or other components required tooperationalize data and AI/ML solutions.
Strong troubleshooting and problem-solving skills across software, data, and cloud environments.
Ability to learn new technologies quickly and apply them to evolving technical and businessrequirements.
Technology Environment
Our AI/ML environment is built primarily on Google Cloud Platform. Current technologies include BigQuery,Dataproc, Dataflow, Vertex AI, Gemini and related GCP compute, storage, orchestration, data, and AIservices.3
Experience with GCP is preferred, but deep experience with every component of our current stack is notrequired. We value strong cloud, software engineering, Python, data, and MLOps fundamentals and theability to quickly learn and apply new technologies.
Preferred Qualifications
Experience with Google Cloud Platform and cloud-native AI/ML architectures.
Experience operationalizing generative AI, LLM, or agentic solutions.
Familiarity with model registries, ML pipelines, model monitoring, experiment tracking, and modellifecycle management.
Experience with infrastructure automation and modern cloud deployment approaches, includingmanaged cloud services and/or containerized applications.
Experience designing reusable technical solutions that support multiple models, use cases, orclients.
Healthcare experience and familiarity with medical claims, pharmacy claims, eligibility, clinical, ormember data.
