Infrastructure Engineer/SRE

Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta’s unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world’s leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day. Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world’s leading investors, including a16z, Greylock, and Sequoia. Cresta’s leadership includes some of the leading minds in AI today. Our CEO, Ping Wu, founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences. Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta. About the role:

As a member of the infrastructure team you are responsible for designing, building, and advancing our core infrastructure that allows the engineering team to execute quickly, productively, and securely. You will join a collaborative but highly autonomous working environment in which each member has a defined role with clear expectations, as well as the freedom to pursue projects they find interesting. #### Responsibilities:

• Developer Toolchain. Partner with engineers to build dev tools that empower developer workflows and deployment infrastructure. - Ensure reliability of multi-cloud Kubernetes clusters and pipelines. - Metrics, logging, analytics, and alerting for performance and security across all endpoints and applications. - Infrastructure-as-code deployment tooling and supporting services on multiple cloud providers. - Automate operations and engineering. Focus on automation so we can spend energy where it matters. - Building machine learning infrastructure that enables AI teams to train, test, and deploy on large-scale datasets.