Top AI Jobs in Sydney

Design, build, and deploy production AI applications and agent-based systems using the H2O AI platform. Contribute to end-to-end ML workflows (data prep, feature engineering, training, evaluation, deployment, monitoring), develop backend services and APIs, support customer implementations, run technical workshops, and collaborate with cross-functional teams to deliver measurable enterprise AI solutions.

Design, build, and deploy production-grade AI applications and agentic systems. Own end-to-end AI solutions including data preparation, feature engineering, model development, deployment, monitoring, and governance. Architect GenAI systems (RAG, LLMs/SLMs, agents), build backend services/APIs, guide customers on adoption and responsible AI, and deliver technical enablement.

Work with a small engineering team to write, test, and ship production-ready code across front-end and back-end (React, Node.js). Collaborate with stakeholders, support QA, customer onboarding and deployments, learn from the Lead Engineer, and contribute to product features with autonomy in a hybrid startup environment.

The role requires defining and evolving the architecture for enterprise platforms, collaborating with teams on integration patterns, and ensuring solutions are scalable and secure.

The Director of AI Engineering will lead AI capabilities, including development and evaluation of AI systems, while managing a team and collaborating with cross-functional departments.

As a Senior Software Engineer at Fetch, you'll build AI-driven products to improve pet health, mentor engineers, and develop event-driven architectures. You'll collaborate with teams to deliver impactful features and ensure reliability and observability in the systems you create.

Join Fetch as a Software Engineer to build AI-driven products that enhance pet health and streamline veterinary care, working with a collaborative team.

As Head of ANZ Applied AI, you'll build a team and drive AI adoption for enterprises in Australia and New Zealand, ensuring customer success through technical guidance and innovative solutions.

Provide pre-sales architecture and technical consulting for AI/HPC networking and infrastructure across Australia/NZ. Lead scoping workshops, design networks for large GPU clusters, assess data center fit-out, build OEM relationships, and deliver high-quality AI infrastructure solutions.

Embed with clients to discover high-value agent use cases, design and build production-grade agentic architectures (orchestration, data hydration, semantic layers), implement verification and governance, manage stakeholder expectations, and capture reusable playbooks and eval suites for future engagements.

Design and build connectors, tool integrations, and real-time data pipelines to enable LLM-based agents to act on analytics, SEO, content, and marketing platforms. Develop reusable agent skills, evaluate agent performance against MAU and ARR, iterate with Country Managers, replace manual processes with live tooling, and document frameworks for repeatable market deployment.

Own and build a governed library of production-ready AI skills: write and test prompts and workflows, manage versioned repositories, create testing and release standards, track adoption and performance, and embed responsible AI practices. Work cross-functionally to translate requirements, join skills into end-to-end systems, maintain documentation and audits, and iterate based on real-world use.

Lead delivery of AI-enabled solutions: design, integrate and operationalise cloud and third‑party AI services on Azure. Ensure scalability, compliance, responsible AI, mentor engineers, and provide vendor oversight to deliver measurable business outcomes.

Embed with enterprise clients to define outcomes and rapidly prototype production-grade agentic AI systems. Design RAG pipelines, multi-agent orchestration, and LLM assistants using Amazon Bedrock and SageMaker. Deploy end-to-end integrations into enterprise platforms while building responsible AI controls, evaluation pipelines and compliance for regulated industries.

Manage and grow InfoTrack's AI legal technology partner channel, drive adoption of MCP-enabled AI solutions in law firms, own partner lifecycle from negotiation to enablement, collaborate globally to expand markets, gather market intelligence to shape product roadmap, and deliver measurable revenue and adoption outcomes.

Lead end-to-end delivery and production readiness of AI solutions on Azure or AWS. Provide technical leadership, architecture governance, CI/CD and IaC oversight, DevOps adoption, stakeholder engagement, and team mentoring. Ensure secure, scalable, resilient deployments, coordinate onshore/offshore teams, manage risks, and support hypercare/production support.

As a Senior Applied AI Solution Engineer, you'll develop AI applications, consult with clients, lead projects, ensure ethical practices, and maintain AI solutions.

Design, build, and deploy AI applications using large language models. Involve client consultation, technical leadership, and quality assurance while ensuring ethical AI practices.

AI Engineers will design and implement AI/ML solutions on AWS and Azure, build ML models and data pipelines, and ensure best coding practices while mentoring junior engineers.

Support building AI-powered, no-code/low-code workflow automations across marketing, sales, customer success, and internal ops. Help automate CRM and repetitive tasks, assist AI-assisted research, test and document workflows, and collaborate with cross-functional teams to improve business processes.

Support marketing and growth teams to create AI-generated content, plan and schedule social posts, build email campaigns and automation, manage CRM segmentation, repurpose content, conduct SEO and competitor research, monitor campaign performance and reporting, document processes and prompts, and assist with webinar and partner marketing.

Work with AI engineers to build, test, and improve production AI workflows that automate business processes, integrate with APIs and data stores, develop simple LLM-based applications, optimize prompts and structured outputs, and deliver at least one functional AI automation project during the internship.

Act as the primary technical bridge to enterprise customers: drive technical sales, architect solutions, lead demos and trials, provide packet- and network-level troubleshooting, automate workflows with scripting and AI tooling, and feed product and engineering with field feedback to improve ThousandEyes AI-driven assurance.

Lead design, development, testing, and App Store submission of production-scale iOS features in Swift. Collaborate cross-functionally, enforce code quality via reviews and refactoring, maintain technical documentation, and adopt AI-assisted tooling to improve productivity across large, user-facing modules.

Design, build, and deploy scalable Generative AI and LLM production applications on GCP. Implement RAG architectures with vector databases, develop high-throughput APIs and microservices, collaborate with cross-functional teams, and apply prompt engineering, model optimization, and performance evaluation to ensure reliable AI solutions.