Senior Software Engineer – Deployment Reliability & AI Automation

Contract: 6 months
Location: Australia – Remote, Sydney / Melbourne / Brisbane
Rates: $800-950 a day inc super
Aus Citizenship or Permanent Residency Required

About the Role

We're seeking two Senior Software Engineers to join a large-scale Delivery Engineering team responsible for the reliability of a business-critical continuous delivery platform.

This is a hands‐on engineering role focused on diagnosing and resolving deployment failures across complex, high-volume CI/CD pipelines. You'll investigate failures, navigate unfamiliar codebases, identify root causes and implement fixes through code changes and pull requests.

The role sits at the intersection of software engineering, cloud infrastructure, DevOps and AI automation. Beyond resolving individual failures, you'll document solutions in a structured format that enables AI agents to learn from recurring patterns and autonomously resolve similar issues in the future.
This is an chance to make a meaningful impact on how software is deployed at scale, helping improve engineering efficiency and strengthen AI‐driven deployment recovery.

Key Responsibilities

- Diagnose and resolve deployment failures: Investigate failed CI/CD pipelines, using logs, metrics and pipeline output to identify root causes across application code, infrastructure configuration and deployment processes.
- Implement code‐level fixes: Work across unfamiliar repositories, review existing code and submit targeted pull requests to resolve issues safely and efficiently.
- Troubleshoot AWS provisioning: Identify and resolve deployment issues relating to cloud infrastructure, configuration, IAM, networking and infrastructure‐as‐code.




- Debug post‐deployment checks: Investigate failing health checks, smoke tests and integration tests, determining whether the issue lies with the check itself, the service or a genuine regression.
- Work across the stack: Apply a software engineering mindset to live systems, understanding how applications built, deployed, monitored and operated in production.
- Build AI‐ready documentation: Create structured failure records capturing the failure signature, root cause, resolution and verification evidence.
- Enable AI‐driven remediation: Turn recurring failure patterns into reusable AI agent skills, instructions and evaluation cases, helping automate future diagnosis and recovery.
- Improve operational playbooks: Contribute to runbooks and troubleshooting guides, using recurring issues to improve deployment reliability and reduce manual intervention.
- Collaborate across engineering teams: Work with service owners and platform engineers to implement and validate fixes, ensuring changes are reviewed and approved by the appropriate teams.

Essential Skills & Experience

- Strong software engineering background, with approximately 6+ years of professional experience and the ability to work independently across unfamiliar codebases.
- Hands‐on CI/CD and deployment troubleshooting experience, ideally with Spinnaker or comparable tools such as Argo CD,



Harness or Jenkins.
- Strong AWS experience, particularly troubleshooting infrastructure provisioning and configuration failures involving services, IAM, networking and Terraform or CloudFormation.
- Kubernetes and containerisation experience, including container image builds, deployments and base image upgrades.
- Strong debugging skills, with experience using logs, metrics and monitoring tools such as Datadog, Splunk or similar to establish root causes.
- Experience with health checks and deployment verification, including HTTP/gRPC endpoints, smoke tests, synthetic monitoring or integration tests.
- Proficiency in at least one programming language such as Go, Python, Java, Kotlin or TypeScript, with the ability to make safe, targeted code changes across different technologies.
- Practical experience with AI coding assistants or agent frameworks, such as Claude Code, Codex or comparable tools. You should be comfortable using AI to support engineering workflows and translating successful troubleshooting processes into reusable agent instructions, skills or evaluation cases.
- Clear technical documentation and communication skills, with the ability to explain failures, document solutions and share knowledge with other engineers.

Desirable Experience

- Experience working within a large‐scale SaaS, cloud or technology organisation with high‐volume deployment pipelines.
- Exposure to AI agent orchestration, sub‐agents, reusable agent skills or internal agent frameworks.
- Experience developing evaluation datasets or test harnesses for AI agents.
- Experience remediating container image vulnerabilities, dependency issues or image upgr

📌 Senior Software Engineer (Adelaide)
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