Top AI Jobs in Sydney

APAC specialist overlay driving growth of Sprinklr's CCaaS and Agentic AI: partner with account teams, create pipeline, lead solution selling, engage senior CX and IT stakeholders, and build partner ecosystem to win complex enterprise deals.

Lead AMP's enterprise AI risk program: shape governance, inventory, observability, assurance and reporting; embed AI risk controls across business, technology, data, security and risk; influence senior stakeholders and committees; uplift frameworks, policies and controls to meet regulatory obligations while enabling responsible innovation.

Provide specialist legal advice on cyber preparedness and incident response, complex data/privacy matters, AI governance, regulatory investigations and enforcement. Work with cross-firm teams to advise boards and executives, manage large matters, lead teams, and support business development.

Lead design and delivery of scalable data and AI platforms across Azure and Databricks. Architect production-grade ML/LLM solutions (RAG, agentic patterns), build batch and streaming pipelines, formalize medallion/lakehouse standards, implement MLOps/DataOps (CI/CD, monitoring, orchestration), embed Responsible AI and governance, and provide technical leadership, mentoring, and cross-functional delivery.

As an Applied AI Engineer, you'll build AI agents and experiences from concept to production, collaborating across teams and optimizing systems for usability and quality.

Define and execute the product roadmap for AI-driven on-chain monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, ensure timely delivery of features, and communicate roadmap progress to stakeholders to improve blockchain security offerings.

Provide on‑demand coaching and small‑group training to accelerate enterprise AI adoption (Microsoft Copilot, Gemini, Claude). Develop micro‑training resources, monitor usage, collect feedback, guide responsible AI governance, and help employees apply AI to everyday tasks.

Partner with business stakeholders to identify AI opportunities, rapidly develop proofs of concept and scalable AI-enabled solutions using approved AI platforms, low-code and automation tools. Drive adoption through workshops, demos, reusable prompts/templates, and coaching while ensuring solutions meet Responsible AI, security, privacy, and governance standards and deliver measurable business value.

Lead end-to-end customer deployments of Cresta's AI SaaS platform across APAC, manage complex, customer-facing projects, define success metrics, ensure smooth launches, drive adoption, and collaborate with product, engineering, and GTM teams to scale AI solutions.

Own and evolve the AI-native platform and developer experience: build CI/CD, build/test/deploy loops, agent-safe sandboxes, cloud infra and cost controls, SLOs and observability, incident response, and platform capabilities that multiply engineering productivity across teams.

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

Lead technical direction for AI-native agent products, solve high-leverage engineering problems, improve reliability and evaluation, partner with product and customer teams, mentor engineers, and ship production-grade autonomous agent features rapidly.

Build and ship AI-native, agentic products end-to-end: design LLM-powered workflows, prototype with frontier models, collaborate with product/design/customers, ensure reliability through continuous shipping and on-call ownership, mentor engineers, and iterate based on customer feedback to scale intelligent agents into production.

Serve as a technical strategic advisor embedding with strategic accounts to design and deliver enterprise agentic AI programs. Quantify and report value targets, manage program delivery and onboarding at scale, build centers of excellence, and ensure visibility on consumption and value metrics while maintaining strong customer relationships.

Embed with enterprise engineering teams to deploy and integrate Devin and agentic workflows, run workshops and pair-programming, quantify productivity impact, and scale customer learnings into repeatable playbooks and enablement materials.

Lead Agentic Solution sales and presales teams to drive revenue for NiCE's AI and digital CX solutions (standalone or with CXone). Build territory plans, exceed targets, identify new opportunities, collaborate cross-functionally, analyze market trends, monitor sales metrics, and manage team hiring and separations.

Support sales by running discovery calls, technical deep dives, demos and POCs for AI and contact-center solutions; design recommended technical solutions, analyze requirements, provide technical guidance and training, and stay current on competitive landscape.

Build and operate production AI systems that ingest event-level revenue and telemetry, ensure data quality, run experimentation, investigate failures, and provide agent-enabled analytics and automation to improve business decisions.

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.

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.

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.

Own and improve an embedded Linux Edge AI sensor platform: Yocto builds, kernel/drivers, device trees, hardware integration (cameras, microphones, modems, sensors), OTA and provisioning, device security, profiling and optimizing Edge ML inference (NPU/quantisation), audio analysis, and backend services for fleet diagnostics, observability and deployment. Collaborate with hardware, AI, cloud and manufacturing teams from concept through field deployment.

Drive visual AI quality by annotating and evaluating large visual datasets, developing scoring rubrics, identifying failure modes, and translating design critique into structured feedback for ML engineers. Use generative tools to prototype, contribute to taxonomies, and maintain high standards of annotation documentation to improve model performance and aesthetic alignment.

As an AI/ML Engineer, you will develop and optimize AI solutions for renewable energy assets, collaborating with a skilled team to deploy algorithms in real-world projects.