Staff GCP engineer

CVS Health•Work At Home-Illinois, IL

1d•$130,295 - $260,590

About The Position

CVS Health is seeking a GCP Platform Support Engineer with expertise in Site Reliability Engineering (SRE) and CI/CD practices. The role supports the reliability, availability, and performance of cloud-based applications and services on Google Cloud Platform (GCP), partnering with development teams to build CI/CD pipelines, monitor system performance, and troubleshoot issues. The engineer will build and maintain GCP infrastructure and services that enable application engineers to develop, deploy, and scale AI agents, plugins/tools, and knowledge-based applications securely. The role also enables integrations with LLMs, vector and knowledge stores, APIs, and enterprise plugins, while establishing reusable patterns, security controls, observability, and best practices for AI agent development. This is a full-time position. The stated pay range is $130,295 to $260,590, with eligibility for a CVS Health bonus, commission, or short-term incentive program and an equity award target. Benefits include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility. The application window is anticipated to close on November 13, 2026.

Requirements

At least 3 years of experience with GCP services, including Compute Engine, Kubernetes Engine, Cloud Storage, and Cloud Functions.
At least 3 years of experience with SRE principles and practices, including monitoring, incident management, and performance optimization.
At least 3 years of experience with CI/CD tools and practices, including Google Cloud Build, Jenkins, GitLab CI, or similar.
At least 3 years of experience with container orchestration technologies such as Kubernetes and Docker.
At least 3 years of experience with scripting languages such as Python or Bash and IaC tools such as Terraform or Google Deployment Manager.
At least 3 years of experience with monitoring and logging tools such as Google Cloud Monitoring, Prometheus, or Grafana.
Strong analytical and troubleshooting skills, including the ability to work under pressure.
Excellent verbal and written communication skills and the ability to collaborate effectively with cross-functional teams.
Knowledge or experience with Vertex AI, Gemini, Agent Development Kit (ADK), AI agents, MCP/plugins, RAG, Vector Search, Cloud Run, and AI security and governance.
A bachelor's degree in computer science, information systems, engineering, or a similar field; for each year of college degree not completed, one year of professional work experience is required in addition to the required years of experience.

Nice To Haves

Google Cloud Professional Cloud Architect or Professional DevOps Engineer certification.
Familiarity with Agile development methodologies and practices.
Experience with ETL/data pipeline and orchestration tools such as Cloud Composer, Cloud Data Fusion, Dataflow, and Dataproc.
Knowledge of database technologies such as Cloud SQL and BigQuery and of data management practices.

Responsibilities

Provide technical support for GCP-based applications and services to ensure high availability and performance.
Automate repetitive tasks and processes using scripting languages and Infrastructure as Code tools.
Implement cloud security best practices, including identity and access management, data encryption, and networking security.
Monitor and manage cloud resources, including virtual machines, containers, databases, and networking components.
Design, implement, and maintain CI/CD pipelines using Google Cloud Build, Jenkins, GitLab CI, or similar tools.
Set up and manage monitoring and alerting systems to ensure system health and performance.
Participate in incident response and post-mortem analysis, identify root causes, and implement preventive measures.
Apply SRE practices, including service level objectives, service level indicators, and error budgets.
Build and maintain GCP infrastructure and services that enable secure development, deployment, and scaling of AI agents, plugins/tools, and knowledge-based applications.
Enable integrations with LLMs, vector and knowledge stores, APIs, and enterprise plugins; establish reusable patterns, security controls, observability, and AI agent development best practices.
Collaborate with development, QA, and operations teams to support smooth application deployment and operation.
Create and maintain documentation for system architecture, processes, and procedures.