" \u003cdiv class=\"ja-job-details\"\u003e \u003ch2 class=\"title\"\u003eSenior Software Engineer\u003c/h2\u003e \u003cdiv class=\"meta\"\u003e \u003cul class=\"classifications\"\u003e \u003cli data-id=\"21944\"\u003e Data Engineering, Cloud Data Platforms \u0026amp; DevOps \u003c/li\u003e \u003cli data-id=\"21946\"\u003e Sydney \u003c/li\u003e \u003cli data-id=\"21947\"\u003e Contract or Temp \u003c/li\u003e \u003c/ul\u003e \u003cp class=\"date-posted\"\u003e 29/10/2026 \u003c/p\u003e \u003c/div\u003e \u003cul class=\"bullet-points\"\u003e \u003cli\u003e6 month contract, scope to extend\u003c/li\u003e \u003cli\u003eRemote work, Sydney / Melbourne / Brisbane\u003c/li\u003e \u003cli\u003eWork with one of Aus biggest Tech names\u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"description\"\u003e \u003cstrong\u003eSenior Software Engineer \u0026ndash; Deployment Reliability \u0026amp; AI Automation\u003c/strong\u003e\u003cbr\u003e \u003cstrong\u003eContract:\u003c/strong\u003e 6 months\u003cbr\u003e \u003cstrong\u003eLocation:\u003c/strong\u003e Australia \u0026ndash; Remote, Sydney / Melbourne / Brisbane\u003cbr\u003e \u003cstrong\u003eRates: \u003c/strong\u003e$800-950 a day inc super\u003cbr\u003e Aus Citizenship or Permanent Residency Required\u003cbr\u003e \u003cbr\u003e \u003cstrong\u003eAbout the Role\u003c/strong\u003e\u003cbr\u003e We\u0026rsquo;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.\u003cbr\u003e\u003cbr\u003e This is a hands-on engineering role focused on diagnosing and resolving deployment failures across complex, high-volume CI/CD pipelines. You\u0026rsquo;ll investigate failures, navigate unfamiliar codebases, identify root causes and implement fixes through code changes and pull requests.\u003cbr\u003e\u003cbr\u003e The role sits at the intersection of software engineering, cloud infrastructure, DevOps and AI automation. Beyond resolving individual failures, you\u0026rsquo;ll document solutions in a structured format that enables AI agents to learn from recurring patterns and autonomously resolve similar issues in the future.\u003cbr\u003e This is an opportunity to make a meaningful impact on how software is deployed at scale, helping improve engineering efficiency and strengthen AI-driven deployment recovery.\u003cbr\u003e \u0026nbsp;\u0026nbsp;\u003cbr\u003e \u003cstrong\u003eKey Responsibilities\u003c/strong\u003e\u003cbr\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eDiagnose and resolve deployment failures:\u003c/strong\u003e Investigate failed CI/CD pipelines, using logs, metrics and pipeline output to identify root causes across application code, infrastructure configuration and deployment processes.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eImplement code-level fixes:\u003c/strong\u003e Work across unfamiliar repositories, review existing code and submit targeted pull requests to resolve issues safely and efficiently.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eTroubleshoot AWS provisioning:\u003c/strong\u003e Identify and resolve deployment issues relating to cloud infrastructure, configuration, IAM, networking and infrastructure-as-code.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eDebug post-deployment checks:\u003c/strong\u003e Investigate failing health checks, smoke tests and integration tests, determining whether the issue lies with the check itself, the service or a genuine regression.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eWork across the stack:\u003c/strong\u003e Apply a software engineering mindset to live systems, understanding how applications are built, deployed, monitored and operated in production.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eBuild AI-ready documentation:\u003c/strong\u003e Create structured failure records capturing the failure signature, root cause, resolution and verification evidence.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eEnable AI-driven remediation:\u003c/strong\u003e Turn recurring failure patterns into reusable AI agent skills, instructions and evaluation cases, helping automate future diagnosis and recovery.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eImprove operational playbooks:\u003c/strong\u003e Contribute to runbooks and troubleshooting guides, using recurring issues to improve deployment reliability and reduce manual intervention.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eCollaborate across engineering teams:\u003c/strong\u003e Work with service owners and platform engineers to implement and validate fixes, ensuring changes are reviewed and approved by the appropriate teams.\u003c/li\u003e \u003c/ul\u003e \u0026nbsp;\u0026nbsp;\u003cbr\u003e \u003cstrong\u003eEssential Skills \u0026amp; Experience\u003c/strong\u003e\u003cbr\u003e \u003cul\u003e \u003cli\u003e\u003cstrong\u003eStrong software engineering background\u003c/strong\u003e, with approximately 6+ years of professional experience and the ability to work independently across unfamiliar codebases.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eHands-on CI/CD and deployment troubleshooting experience\u003c/strong\u003e, ideally with Spinnaker or comparable tools such as Argo CD, Harness or Jenkins.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eStrong AWS experience\u003c/strong\u003e, particularly troubleshooting infrastructure provisioning and configuration failures involving services, IAM, networking and Terraform or CloudFormation.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eKubernetes and containerisation experience\u003c/strong\u003e, including container image builds, deployments and base image upgrades.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eStrong debugging skills\u003c/strong\u003e, with experience using logs, metrics and monitoring tools such as Datadog, Splunk or similar to establish root causes.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eExperience with health checks and deployment verification\u003c/strong\u003e, including HTTP/gRPC endpoints, smoke tests, synthetic monitoring or integration tests.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eProficiency in at least one programming language\u003c/strong\u003e such as Go, Python, Java, Kotlin or TypeScript, with the ability to make safe, targeted code changes across different technologies.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003ePractical experience with AI coding assistants or agent frameworks\u003c/strong\u003e, 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.\u003c/li\u003e \u003cli\u003e\u003cstrong\u003eClear technical documentation and communication skills\u003c/strong\u003e, with the ability to explain failures, document solutions and share knowledge with other engineers.\u003c/li\u003e \u003c/ul\u003e \u003cstrong\u003eDesirable Experience\u003c/strong\u003e\u003cbr\u003e \u003cul\u003e \u003cli\u003eExperience working within a large-scale SaaS, cloud or technology organisation with high-volume deployment pipelines.\u003c/li\u003e \u003cli\u003eExposure to AI agent orchestration, sub-agents, reusable agent skills or internal agent frameworks.\u003c/li\u003e \u003cli\u003eExperience developing evaluation datasets or test harnesses for AI agents.\u003c/li\u003e \u003cli\u003eExperience remediating container image vulnerabilities, dependency issues or image upgrade failures.\u003c/li\u003e \u003cli\u003eFamiliarity with production systems where engineers are responsible for building, deploying, monitoring and maintaining the services they develop.\u003c/li\u003e \u003c/ul\u003e \u003cstrong\u003eThe Ideal Candidate\u003c/strong\u003e\u003cbr\u003e You\u0026rsquo;re a hands-on, senior engineer who combines strong coding ability with a practical understanding of cloud infrastructure and production operations.\u003cbr\u003e You enjoy getting into the detail of complex technical problems, reading other people\u0027s code, following evidence through logs and configuration, and working out exactly why something has failed. You can make targeted changes without introducing unintended consequences, even when working in unfamiliar systems.\u003cbr\u003e You\u0026rsquo;re also interested in how AI can improve engineering workflows. Rather than simply using AI to write code, you understand the importance of capturing repeatable troubleshooting knowledge so AI agents can learn to diagnose and resolve issues independently.\u003cbr\u003e \u003cstrong\u003eThis is not a traditional operations-only or monitoring role.\u003c/strong\u003e The successful engineers will be actively debugging, coding, fixing deployment failures and helping build the foundations for AI-powered remediation.\u003cbr\u003e \u003cstrong\u003eWhy Apply?\u003c/strong\u003e\u003cbr\u003e \u003cul\u003e \u003cli\u003eWork on a large-scale continuous delivery platform supporting engineering teams across a major technology environment.\u003c/li\u003e \u003cli\u003eSolve complex, real-world problems across software engineering, AWS, Kubernetes and CI/CD.\u003c/li\u003e \u003cli\u003eHelp shape the next generation of AI-assisted engineering and automated deployment recovery.\u003c/li\u003e \u003cli\u003eWork closely with experienced platform engineers in a collaborative, technically challenging environment.\u003c/li\u003e \u003cli\u003eTake ownership of meaningful engineering problems, with the opportunity to see recurring issues progressively automated through AI.\u003c/li\u003e \u003c/ul\u003e \u0026nbsp;\u0026nbsp; \u003c/div\u003e \u003cdiv class=\"apply\"\u003e \u003ca class=\"back-link\" href=\"#\" title=\"Back to search results\"\u003eBack to search results\u003c/a\u003e \u003cinput type=\"button\" class=\"ja-button\" data-job-id=\"4482223\" value=\"Apply Now\" title=\"Apply Now\" /\u003e \u003c/div\u003e \u003c/div\u003e "