AI TECHNICAL LEAD DEVOPS – INFORMATION TECHNOLOGY –SYDNEY
About the role
As a Lead DevOps Engineer you will lead the adoption of AI-augmented DevOps and AIOps practices, embedding approved AI tooling across the toolchain to accelerate IaC, scripting and pipeline automation, and applying AI and machine learning to monitoring and operations for anomaly detection, predictive scaling and self-healing remediation.
The DevOps team is responsible for managing (IaC) templates, and setting the stage for an automated, efficient pipeline that accelerates innovation while maintaining high-quality standards.
As an engineering lead you will technically lead others across the team to further the automation practice at Allianz Technology, providing coaching to those around you embedding best practice development processes and enhancing productivity.
AI-Augmented Automation and Toolchain Enablement:Apply AI and generative-AI tooling across the DevOps toolchain to accelerate IaC, scripting, pipeline configuration and documentation.
Select and standardise AI tooling, evaluating for genuine productivity gains, security, data handling, compliance governing adoption.
AI/ML Platform Enablement (MLOps/LLMOps):Build and operate the pipelines, environments and guardrails that allow teams to train, deploy, serve and monitor AI/ML and LLM-based services, including model registries, CI/CD for models, vector stores and GPU/compute provisioning, ensuring they are reproducible, observable and production-ready.
Infrastructure Design and Management:Develop and maintain scalable, resilient, and secure infrastructure solutions.
Implement infrastructure as code (IaC) using tools like Terraform, Ansible, or CloudFormation.
Continuous Integration, Deployment and AI Quality Gates (CI/CD):Design, implement and manage CI/CD pipelines for efficient, reliable code deployment across environments, and embed AI-assisted quality controls with defined quality gates and human validation so AI-assisted output is verified before promotion to production.
Monitoring, Performance and Intelligent Operations (AIOps):Implement monitoring and logging to ensure system health and performance and apply AI and machine learning to operations to improve resilience and reduce mean‐time‐to‐recovery, retaining human-in-the-loop control over autonomous actions.
Security, Compliance and Responsible AI:Ensure infrastructure and processes comply with security best practices and regulatory requirements,



implementing appropriate security controls, and act as first line of defence for responsible AI use — guarding against data misuse and bias, securing AI tools and agents against risks such as prompt injection and insecure output handling, and retaining transparent human accountability for AI‐ and automation‐driven outcomes, recognising that responsibility for production incidents does not transfer to the tooling.
Cost, Efficiency and Impact Management:Monitor and optimise cloud, infrastructure and AI costs (FinOps for AI), proposing cost‐effective solutions that balance automation throughput against spend without compromising performance or security, and track DORA and operational metrics (deployment frequency, lead time, change‐failure rate and MTTR) to confirm AI and automation deliver genuine reliability and productivity gains rather than added risk or technical debt.
Documentation and Training:Maintain comprehensive documentation of infrastructure, processes, and configurations.
Provide training and support to team members on DevOps tools and best practices.
Design and implement for IT control compliance including security, resilience and risk management controls.
About you
AI Tooling Governance and Cost Control:Knowledge of responsible‐AI governance and auditability, approved‐tooling standardisation across the practice, and AI cost‐management techniques such as token tracking, caching, model routing and GPU/compute optimisation (FinOps for AI).
AI Security:Understanding of AI‐specific security risks, including the OWASP Top 10 for LLM Applications, and securing AI tools and agents within the toolchain and software supply chain, alongside established practices such as SAST, SCA and DAST.
Understanding of compliance and regulatory and standards including APRA CPS 230 (Operational Risk Management) and CPS 234 (Information Security), GDPR and the Privacy Act **** and the ability to embed risk‐based controls in DevOps design and processes.
Cloud Platforms:Proficiency in AWS, Azure or GCP,



with experience designing and managing cloud infrastructure and services.
Infrastructure as Code (IaC):Expertise in Terraform, Ansible and CloudFormation.
Ability to write and maintain code for automated infrastructure provisioning and management.
Continuous Integration and Continuous Deployment (CI/CD):Experience with CI/CD tools, ideally Jenkins and GitHub Actions, and the ability to design, implement and manage CI/CD pipelines for automated software delivery.
Containerization and Orchestration:Proficiency with Docker, and orchestration tools Kubernetes, Docker Swarm or OpenShift.
MLOps/LLMOps Platforms:Experience building CI/CD and platform capabilities for AI/ML workloads, including model registries, feature stores, vector databases, model serving and GPU/compute provisioning, with familiarity with MLOps/LLMOps tooling and retrieval‐augmented generation (RAG) patterns.
Scripting and Automation:Strong scripting skills in Python, Bash or PowerShell.
Monitoring and Logging:Knowledge of monitoring tools (Dynatrace, Prometheus, Grafana) and logging solutions (Splunk, ELK Stack).
Version Control:Proficiency in version control systems like Git and SVN.
Experience in branching, merging, and managing code repositories.
Networking:Understanding of networking concepts, protocols, and security best practices.
Experience in implementing network security measures, firewalls, and VPNs.
Configuration management:Experience with Puppet or Ansible.
Ability to manage and automate configuration changes across environments.
Databases:Knowledge of SQL and NoSQL databases (DB2, MySQL, PostgreSQL, MongoDB).
Experience in managing database backups, replication, and performance tuning.
Able to design and deploy solutions conforming to AWS‐well‐architected for high availability and performance.
Disaster Recovery and Backup:Experience designing and implementing disaster recovery plans, and knowledge of backup and recovery processes to ensure data integrity.
AI-Augmented Engineering and AIOps:Hands‐on experience using AI and generative‐AI coding and operations assistants to generate and review IaC, pipeline configuration and scripts, and applying AIOps and ML techniques for anomaly detection, alerting and automated remediation, with the judgement to validate and correct AI output.
A passion for leveraging emerging technologies to stay ahead in a rapidly evolving digital landscape.
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📌 Ai Technical Lead Devops (New South Wales)
🏢 Allianz
📍 New South Wales