DEVOPS ARCHITECT - AWS

This role modernizes our software delivery platform and evolves Developer Experience (DevXP) toward an AI-enabled, agentic model. It blends CI/CD platform engineering with practical AI application ? focused on real engineering problems such as pipeline migration, automation, code understanding, and testing enablement. This is not an AI research role.

Enable faster, safer software delivery with minimal friction by building a standardized, AI-enabled developer platform.

Migrating Jenkins to GitHub Actions

Developing reusable workflows and standards

Building "agent-ready" repositories

Designing AI-assisted engineering workflows

Implementing quality gates and guardrails

Driving developer self-service and platform adoption

Migrate legacy Jenkins pipelines to GitHub Actions.

Build reusable workflows, templates, and standards to reduce fragmentation.

Improve visibility and observability across all delivery flows.

Apply AI to accelerate migrations, code analysis, testing, and documentation.

Create safe, repeatable AI workflows backed by robust validation layers.

Define and implement repository standards covering metadata, documentation, instructions, and templates.

Ensure AI tools have sufficient context to operate effectively without compromising security.

Integrate AI into engineering workflows across GitHub, CI/CD, Jira, and related systems.

Define guardrails around access, validation, peer review, and security.

Focus on high-value, practical use cases that address immediate bottlenecks.

Onboard engineering teams to standardised CI/CD and AI practices.

Provide golden paths, documentation, and starter templates.

Drive adoption through iterative feedback loops and user-centric solutions.

Strong hands-on experience with GitHub Actions and Jenkins.

Proven track record building reusable workflows and developer tooling.

Proficiency in Python, Bash, TypeScript, or Go.

Experience with containers, Kubernetes, and cloud-native delivery practices.

Deep understanding of GitHub workflows including PRs, CODEOWNERS, and repository standards.

Ability to unify fragmented environments and drive enterprise-wide adoption.

Hands-on experience with LLM-powered tooling such as GitHub Copilot or Claude.

Practical application of AI to code understanding, automated testing, and migration tasks.

Experience managing repository-level AI context (e.g., .github/instructions.md or prompt files).

Solid grasp of AI risks including hallucinations, security vulnerabilities, and over-automation.

Experience designing safe, human-in-the-loop AI patterns with manual validation checkpoints.

End-to-End Ownership ? Own the platform modernization roadmap from conception through execution.

Bias for Action ? Deliver tangible tooling and automation, not just high-level strategy.

Pragmatism ? Turn emerging AI capabilities into practical, everyday engineering workflows.

Strategic Influence ? Balance standardization requirements with real-world project constraints.

Leadership by Example ? Influence teams through highly usable solutions and clear, concrete examples.

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