Senior AI Engineer

Senior AI Engineer – Enterprise AI Enablement -Sydney -$160,000 - $180,000 + Super + Bonus Join one of the most forward-thinking teams in Sydney, working within a business that is genuinely AI-first in its approach. There is a defined strategy, real budget, and enterprise-wide commitment to embedding AI across products and operations. We’ve recently partnered closely with the business to appoint their Engineering Manager , and are now supporting the next phase of the build-out. Off the back of that hire, we’re adding multiple engineers to deliver on a pipeline of greenfield AI initiatives . The focus is clear: strong software engineering foundations , with exposure to AI-native environments. This is not a data science role, it’s about engineers who can build the systems and platforms that enable agentic AI at scale . You’ll be working on production-grade systems across multiple use cases, from customer-facing agents and chatbots to internal AI capability , partnering closely with teams responsible for fine-tuning models. A solid understanding of agentic ecosystems and LLMOps will go a long way in helping you succeed. Overview of the Role You’ll be responsible for building and owning scalable backend systems that power enterprise AI solutions. This is a hands-on engineering role, focused on writing high-quality code, automating workflows, and creating the foundations required for AI to operate reliably in production. Operating in a modern DevOps environment, you’ll take end-to-end ownership across design, build, deployment, and ongoing optimisation. You’ll work closely with data and AI teams to integrate models into real-world applications, ensuring they are performant, scalable, and maintainable. Key Skills 5+ years software engineering experience in Agile/DevOps environments , ideally with Python (OOP) Strong end-to-end SDLC experience across design, build, test, and production support Proven ability to build scalable backend systems and write production-grade code Experience with CI/CD pipelines and deployments in containerised environments (Docker/Kubernetes) Exposure to agentic AI / LLM frameworks (e.g. RAG, MCP, Vertex AI, LangChain, LlamaIndex) Understanding of LLMOps practices for deploying, monitoring, and scaling AI models This is a strong opportunity for engineers who want to stay close to the tech, build meaningful systems, and play a key role in how AI is actually delivered across a large enterprise .