Top AI Jobs in Melbourne

Embed with strategic customers to prototype, build, and productionize full-stack AI and agentic applications. Advise on AI strategy, codify repeatable solution patterns, mentor teams, and feed product improvements.

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.

Lead design, build, and deploy production-grade Generative AI solutions. Architect and optimise RAG pipelines and vector DBs, integrate AI via secure APIs/microservices, manage prompt lifecycle, implement MLOps/versioning/monitoring, and mentor engineering teams.

Lead design and delivery of enterprise-scale generative AI and multi-agent systems. Define reference architectures, RAG and vector knowledge layers, integration fabrics, observability, governance, and Responsible AI frameworks. Assess readiness, build roadmaps, coordinate cross-functional teams, and mentor engineering talent.

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.

Senior forward-deployed engineer who leads end-to-end delivery of AI-first, full-stack solutions for enterprise clients. Own discovery through production, embed with client teams, apply Gen-e2 AI-native engineering practices, use LLMs and AI coding tools daily, and modernize .NET applications while ensuring architecture, adoption, and measurable impact.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

The AI Consultant will design and implement AI-driven workflows, develop AI agents, automate processes, and integrate AI systems with third-party platforms, focusing on operational efficiency and support.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Develop and maintain scalable backend services and APIs, collaborate with frontend developers, and lead new features and infrastructure improvements.

The AI Solution Strategist role involves collaborating with product managers and engineers to develop conversational AI agents, driving customer success and supporting AI initiatives. The position requires strategic thinking, project management, and skills in aligning technical and business teams, while focusing on agent design and performance analytics.

Lead presales enterprise architecture for Data/AI and Applications, designing integrated multi-tower solutions (data platforms, GenAI, MLOps, application modernization, cloud, integration, AIOps). Drive account planning, C-suite workshops, bid leadership, value cases, demos, portfolios, and in-contract innovation while coaching teams and ensuring delivery and commercial alignment.

Manage early-stage grantmaking for the Transformative AI Fund: evaluate applications, recommend grants, source opportunities, support grantees, build partnerships, represent the fund, and develop systems and LLM-assisted tooling to improve grant processes.

Lead technical discovery and hands-on AI workshops to identify, validate, and prioritize private AI use cases. Assess enterprise data readiness, define technical requirements and success criteria, build lightweight proofs of concept and reference architectures, and hand off validated opportunities to engineering teams. Partner with ecosystem providers (e.g., NVIDIA) and regional pods to scale reusable AI patterns across customers and industries.

Develop and optimize backend architecture for a multi-brain AI trading system. Design and implement AI/ML models and workflows, integrate blockchain and smart contracts, build and maintain APIs, databases, and cloud infrastructure, perform code review, testing, debugging and performance optimization, and ensure security, data integrity, and production readiness.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Design, build and maintain automation and AI/LLM integrations to reduce manual work, improve accuracy and security, connect business systems, create dashboards, and enforce governance, documentation and testing so tools are reliable and auditable.

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.

Lead company-wide AI adoption by selecting tools, running workshops, diagnosing team problems, building reusable workflows/automation, tracking tool usage and license ROI, and writing responsible-use guidelines while partnering across Creative, Marketing, Technology, Finance and People teams.

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.