Top AI Jobs in Sydney

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Lead client-facing AI adoption programmes end-to-end: design change strategies, build executive sponsorship and champion networks, measure behaviour change and ROI, run executive workshops and company-wide engagement, codify playbooks, and drive renewal and expansion through sustained adoption.

Define safety, usability, and performance metrics for AI and robotic products; translate VLA and agentic RAG breakthroughs into customer value; engage enterprise adopters for feedback; lead cross-functional teams; own product roadmap and delivery for agentic and robotic deployments.

Design, deploy, and maintain MLOps infrastructure: manage model registries, continuous training loops, and A/B testing. Deploy agentic systems as scalable microservices on Kubernetes and build observability dashboards tracking token usage, latency, and agent reasoning.

Design and optimize multimodal AI systems integrating vision and audio models. Improve voice-to-voice streaming latency, embed vision encoders and audio-native models into agent reasoning, and architect multimodal RAG systems for retrieving insights from videos and PDFs.

Develop and train foundation models for robotics, align text-based reasoning with low-level control policies, design and run zero-shot generalization experiments, collaborate with interdisciplinary teams, and present research findings at conferences.

Design and implement multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows.

Optimize and compress large language and vision models for on-device inference. Develop distillation and hardware-specific compilation pipelines, deploy models to edge devices, and benchmark performance across NPU/GPU architectures to ensure efficient on-device operation.

Design and implement HRI systems: build intent-recognition models from gaze, gesture, and voice; create socially aware navigation algorithms; and design visual and auditory robot feedback and interfaces. Run user studies and prototype AI-driven voice and visual interfaces to validate interaction paradigms.

Lead orchestration and optimization of large-scale LLM pretraining across 1,000+ GPUs. Manage distributed training with PyTorch/DeepSpeed/Megatron-LM, tune networking and memory (InfiniBand/RDMA), and implement checkpointing and robust failure recovery for long-running jobs.

Own end-to-end Instagram Reels production: ideation, scripting, editing, and delivery. Use AI tools to support ideation and production, test formats, optimize for retention, and stay on top of short-form video trends to grow international audiences.

Independent full-cycle recruiter managing sourcing, client engagement, offer negotiation, and placements using AI-driven tools. Acts as commercial advisor, provides market insights, and maintains self-directed billing and activity in a results-only, remote environment.

Perform adversarial testing and red-teaming on large language and multimodal models, implement runtime guardrails and filtering, and help develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Design and ship a 14-domain skills taxonomy and SDK/CLI tooling, operate the MCP server, integrate agent protocols, improve LLM grounding for crypto, run developer hackathons, and produce technical content to grow the ecosystem.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead and improve Microsoft 365, Power Platform, Dynamics 365 and AI capabilities. Manage governance, security, vendor relationships and platform adoption while partnering with stakeholders to deliver productivity, automation and business-aligned solutions.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

The AI Integration Manager will implement AI solutions to enhance operational efficiency, develop automated workflows, and achieve measurable outcomes across the business.

Seeking detail-oriented reviewers for AI training projects, evaluating outputs, generating content, and ensuring cultural relevance. Requires strong analytical skills and attention to detail.

Evaluate and review AI outputs, generate or assess content, and ensure cultural relevance and accuracy in various media for AI projects.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

Provide technical pre-sales and architecture support for AI/HPC networking solutions across Australia and New Zealand. Scope requirements, design high-performance network and data center solutions (including power, cooling, cabling), build OEM relationships, advise customers on architectures supporting large GPU clusters, and identify services opportunities.