SENIOR DEVOPS TECHNICAL LEAD

CI/CD & Platform Engineering

• 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.

AI & Agentic Practices

• 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.

Seniority Expectations

• 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.

Key Responsibilities

CI/CD Modernization

• Migrate legacy Jenkins pipelines to GitHub Actions.
• Build reusable workflows, templates, and standards to reduce fragmentation.
• Improve visibility and observability across all delivery flows.

AI-Assisted Engineering

• Apply AI to accelerate migrations, code analysis, testing, and documentation.
• Create safe, repeatable AI workflows backed by robust validation layers.

Agent-Ready Repositories

• Define and implement repository standards covering metadata, documentation, instructions, and templates.
• Ensure AI tools have sufficient context to operate effectively without compromising security.

Agent Workflows & Guardrails

• 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.

Platform Adoption

• 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.