AI DevOps Engineer

Triskele Labs is a large-scale sovereign Australian cybersecurity provider. We deliver Managed Detection and Response (MDR), Digital Forensics and Incident Response (DFIR), offensive security, and Governance, Risk and Compliance (GRC) services to some of the most regulated and high-value organisations in Australia, across financial services, government, education, healthcare, and critical infrastructure. 

We operate a sovereign 24x7 SOC, a national DFIR practice, and a growing engineering function that builds the tooling, automation, and AI capability our delivery teams rely on. Engineering excellence and operational discipline are core to how we win and keep clients. 

The AI DevOps Engineer is a key role at Triskele Labs. You will sit inside the DevOps team and report to the DevOps Architect, who owns the broader cloud, platform, and DevOps stack. Your mandate is to own the design, deployment, and ongoing operation of our AI and AI enablement capability across the business. 

This is an AI-first role. The majority of your time will be spent building and operating the platforms that host our internal and client-facing AI capability. Primarily this means AWS Bedrock for sovereign access to frontier and open-weight models, with selective self-hosting on AWS where workload economics or specialisation justify it, alongside workflow orchestration and agentic automation, and enterprise search and retrieval. 

You will also contribute to cloud and broader platform work alongside the rest of the DevOps team. 

You will be hands-on, opinionated, and accountable for outcomes. Triskele Labs is a fast-moving environment where engineers are trusted to make calls, ship, and own what they build. 

AI Platform and Enablement (primary focus) 

• Design, deploy, and operate AWS Bedrock as the primary inference platform for sovereign internal and client-facing AI workloads, including model access, integration patterns, throughput management, and quota planning across frontier and open-weight model families. 

• Design, deploy, and operate open-weight LLM hosting on AWS for sovereign and client-facing use cases, including model selection, fine-tuning workflows, and inference optimisation. 

• Own the workflow orchestration and agent automation platform, including hosting, scaling, security, integrations, and enablement of business users building workflows. 

• Own model selection and routing across the AI estate so every workload uses the most cost-effective model that meets the quality bar, including modelling unit economics across managed and self-hosted inference options. 

• Stand up and operate enterprise search, retrieval, and embedding infrastructure, including vector search, index lifecycle, ingestion pipelines, and query performance. 

• Define and enforce patterns for prompt management, evaluation, observability, guardrails, cost control, and model lifecycle across the AI estate. 

• Partner with engineering, product, and delivery leaders to identify, scope, and deliver AI use cases that drive measurable business outcomes. 

Engineering, Scripting, and Automation 

• Write high-quality, production-grade AI-assisted code across Python, TypeScript/Node, and Bash to automate platform operations, integrate systems, and build internal tooling. 

• Design and build APIs and integrations between AI services, internal business systems, and the security tooling stack. 

• Apply infrastructure-as-code (Terraform preferred) and GitOps patterns across the AI and DevOps estate. 

• Embed security, observability, and cost discipline into every platform and pipeline you build. 

Cross-functional Contribution 

• Contribute to the wider DevOps roadmap and on-call rotation as a senior member of the team. 

• Document architecture, runbooks, and standards so the rest of the business can adopt and extend the platforms you build. 

• Coach engineers and non-engineers on safe, effective use of AI tooling and automation. 

• Stay current with the open-weight model ecosystem, hyperscaler AI service roadmaps, and the broader AI platform engineering discipline. 

• Minimum 5 years of professional experience in DevOps, platform engineering, or SRE, with a clear track record of operating production workloads at scale. 

• Extremely strong AWS experience across compute, networking, IAM, data, and AI/ML services. Equivalent depth in Azure also required. 

• Demonstrated experience operating LLMs in production via managed inference services (AWS Bedrock or equivalent) and self-hosting open-weight models on a major hyperscaler, with the judgement to choose between them based on cost, quality, latency, and operational fit. 

• Demonstrated ability to model the unit economics of AI workloads and design cost-effective architectures, including cost-per-task and cost-per-outcome views across multiple model and hosting options. 

• Heavy scripting and AI-assisted software engineering ability across Python and TypeScript/Node, including API design, integration patterns, and asynchronous workloads. 

• Hands-on experience with enterprise search and retrieval platforms, including vector search and retrieval-augmented generation patterns. 

• Working knowledge of low-code workflow orchestration and agent automation platforms. 

• Strong understanding of security fundamentals: identity, secrets, network segmentation, data classification, and threat modelling. 

• Excellent written and verbal communication; able to translate complex platform decisions into outcomes business stakeholders understand. 

• Australian citizen with the ability to obtain and maintain Baseline or NV1 clearance preferred. 

Nice to Have 

• Prior experience in a cybersecurity, MSSP, or MDR environment. 

• Experience with the Microsoft enterprise security and AI stack. 

• Experience operating PaaS and managed backend platforms at production scale, and migrating off them into hyperscaler-native services. 

• Experience building evaluation and observability frameworks for LLM-based systems. 

• Contributions to open-source AI, MLOps, or DevOps tooling. 

• Relevant certifications: AWS Solutions Architect Professional, AWS Certified AI Practitioner, AWS DevOps Engineer, Microsoft Certified Azure AI Engineer Associate, Azure Solutions Architect Expert. 

• A key role in a sovereign Australian cybersecurity business with a clear independent growth strategy through FY27. 

• A genuinely modern, hands-on AI and platform engineering remit, not legacy maintenance dressed up as transformation. 

• Broad exposure across a wide range of infrastructure and security technologies. 

• High-trust, high-accountability culture with direct exposure to executive leadership and real influence on technical direction. 

• Competitive salary aligned to senior engineering market rates, with performance-based incentives. 

• Investment in training, certification, and conference attendance. 

• Hybrid working from our Melbourne office. 

How to Apply 

Please submit a CV and a short cover note outlining the most relevant production AI platform or DevOps work you have led, including the technical decisions you owned and the outcomes delivered. 

Triskele Labs is an equal opportunity employer and welcomes applications from candidates of all backgrounds.