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Mid-level, generalist Platform Engineer in metro with broad skillset attracts many qualified applicants.
Core DevOps skills transferable, but AI-platform, ServiceNow, and observability specifics raise domain sensitivity.
Explicit 5–8 years plus specific DevOps, Kubernetes, CI/CD, and observability expertise increases filtering strictness.
Deploy and maintain lab environments supporting multiple types for development, testing, and staging of the Skylar AI platform.
Manage automated deployment pipelines with continuous integration testing to ensure staging matches production and validate system performance metrics like response times and resource consumption.
Lead end-to-end platform testing including integration, regression, rollback validation, and user journey simulation to prevent breaks and ensure system reliability.
5-8 years of relevant work experience in platform engineering or similar roles.
Proficiency in Node.js and/or Python focusing on automation development, and familiarity with REST/GraphQL APIs.
Solid experience with Docker, Kubernetes, CI/CD pipelines (e.g., GitHub, GitOps), and test automation frameworks (such as Playwright).
Not explicitly mentioned in the JD: degree requirements, explicit onsite/location constraints, or notice period.
Experienced in developing and maintaining complex AI-powered observability platforms with a focus on automation and testing.
Comfortable working cross-functionally across product management, engineering, and data science teams to enable platform usage and issue resolution.
Skillful in integrating telemetry, event logging, service management tools (ServiceNow), and applying AI platform knowledge such as LLM API consumption and prompt engineering.