DevOps with AI Tool
Posted Updated
Description
- Required cloud depth Advanced GCP architecture, containers, Kubernetes, infrastructure as code and Terraform; deep configuration management, reproducibility, drift diagnosis and recovery. Engineer secure, resilient, and maintainable platform capabilities.
- Delivery tooling Deep Harness CI/CD; GitHub Enterprise, GitHub Actions, IDP and Backstage. Assemble evolving enterprise templates; integrate artifacts, promotion, required certifications, gates and validated evidence.
- Tool integration Python automation/orchestration, OpenAPI/API contracts, MCP and local agents. Connect human-driven tools and external systems with controlled access, traceable context and failure handling.
- Agentic controls RAG and agent-assisted assessments where useful; validate retrieval scope, provenance and generated reports. Apply responsible AI and security controls; support downstream control teams and accountable approval decisions.
- Operational feedback Production-service support, observability and incident integration.
- Connect root-cause analysis, triage and treatment to Jira, verification and Confluence guidance; maintain a closed improvement loop.
- Hands-on leadership VS Code, GitHub Copilot, coding, infrastructure review and testing.
- Mentor engineers and contribute architecture decisions.
- Understand DDD, Java/Quarkus, Kafka and CQRS implications for deployment and recovery.
|
|
|