Platform Engineer- Skylar AI Platform
Indexed description
We’re accelerating digital transformation through the power of automation, AI, and analytics — giving IT and business leaders the tools to deliver superior customer experiences, drive efficiency, and innovate with confidence.
Role Overview-
We are seeking a passionate and capable Platform Engineer with experience of 5- 8 years to be part of our Skylar AI Platform team - our next-generation AI-powered observability platform. This role is key to ensuring we maintain regular software updates on a monthly cadence to our Skylar AI platform. You will ensure that the lab environment is always available to the engineering team. You will be the go-to person in Product Engineering for End-to-End testing on Skylar AI, using AI to help solve problems, resolve tickets, and provide clear status updates. You'll enable Product Management, Data Science, Quality, and Engineering teams to use the platform to verify and test as much as possible.
Responsibilities
- Lab Creation: Deploy & maintain a lab environment which will be used for development, testing, and staging. This will build on existing lab work that has been done and will support multiple environment types.
- Deployment pipelines: Automated tests trigger on every commit → validate builds → confirm staging matches production
- Cross-system workflows: Test data flowing from ingestion → Skylar AI processing → telemetry capture → reporting dashboards
- User journeys: Simulate real platform usage end-to-end; catch breaks before users do
- Regression prevention: Maintain test suites that catch when new features break existing functionality
- Performance gates: Automated testing validates response times, throughput, and resource consumption
- Integration testing: Verify third-party APIs, databases, and services work correctly together
- Rollback validation: Tests confirm systems safely recover after failed deployments
- Keeping Versions Current: Setting up and keeping Skylar One/Advisor/Analytics (Dev + Stable), and other data sources current, operating, and in sync.
- Data Generation: Setup and maintain telemetry generation whether from existing sources such as SkylarOne or workload simulations
- Documentation: Ensure that documentation of the environment, processes, and uses are kept up to date
- API use and integration, REST/GraphQL principles
- Node.js and/or Python with a focus on automation development
- Full-stack debugging and troubleshooting
- JavaScript/TypeScript frontend development to be able to work with our front end teams
- Test data generation and management
- CI/CD pipeline automation (GitHub, Gitops, etc)
- Test automation frameworks (Playwright)
- Load/performance testing tools (k6, jmeter, etc)
- Docker and Kubernetes fundamentals
- Monitoring and observability tools (SkylarOne, Prometheus, Grafana, etc)
- Database optimization and query performance to help resolve issues found in testing
- Event logging and metrics collection
- Trace aggregation and analytics platforms (OpenTelemetry)
- Management of ServiceNow
- ServiceNow incident/change management workflows
- ITSM best practices and ticketing
- AI Platform Knowledge
- LLM API consumption and integration
- Prompt engineering basics
- AI system debugging and validation
- Cross-functional partnership with engineering, product, and data science
- Proactive issue identification and escalation
- Technical documentation and knowledge sharing
www.sciencelogic.com
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