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Staff ML Engineer at Xero’s AI Products group designs scalable ML infrastructure and sets technical standards for LLM-powered features used by millions of small business customers.
Design and deploy AI-driven automation for trading workflows, integrating LLMs and RPA tools with legacy banking systems to streamline operations and ensure compliance.
Design and deploy AI agents and copilots to automate Roku TV workflows, integrating LLM orchestration, RAG, and multi-agent systems with Python and cloud tools.
Build and deploy AI infrastructure and services for Xero’s products, turning ML models into scalable production systems that serve millions of small-business users.
Design and lead enterprise AI solutions for Australian clients, building secure LLM/RAG architectures and rapid PoCs while mentoring AI engineers.
Build and deploy AI-powered computer vision models for video analytics, working across edge and cloud environments using Python, OpenCV, and frameworks like TensorFlow or PyTorch.
Lead a team to design and deploy AI and data-science solutions that improve healthcare services, using Python, ML frameworks, and cloud platforms.
Build and deploy ML models in NLP, computer vision, and generative AI, integrating them into production apps via cloud APIs and frameworks like PyTorch and LangChain.
Build and maintain cloud-based data pipelines and warehouses for a property-management SaaS, enabling analytics and reporting across the business.
Build and maintain scalable data pipelines for Roku’s ad-auction platform, ensuring reliable, high-performance data flows for digital advertising.
Build and deploy enterprise AI, Generative AI, and ML solutions for clients in Australia and New Zealand using Python, Java, and cloud platforms like AWS/Azure.
Build and scale a Python-based analytics engine that processes building-system data to cut energy waste and carbon emissions, running on Kubernetes and Docker with AI-driven insights.
Build and deploy AI/ML models for demand forecasting, personalization, and content enrichment using Python and PyTorch, then integrate them into production systems.
Build and maintain scalable data pipelines to process terabytes of raw sensor and video data into clean, versioned datasets for training AI models powering autonomous defense systems.
Design and deploy ML models, LLMs, and analytical pipelines to support litigation, regulatory strategy, and AI/cybersecurity advisory for Fortune 100 clients and governments.
Build and fine-tune large language models and deep-learning systems on Cerebras’ wafer-scale AI hardware to solve real-world customer problems, from training bespoke models to deploying agentic AI.
Build and deploy AI/ML models in Python and SQL to solve business problems, ensuring explainability and governance while collaborating with cross-functional teams.
Build and deploy production AI systems: LLM APIs wrapped in FastAPI, prompt engineering, structured JSON outputs, and observability for classification and agent workflows.
Build and optimize large language models for healthcare applications using fine-tuning, RAG pipelines, and production deployment in a collaborative AI team.
Build AI models that turn drone and satellite imagery into ecological insights—classifying land cover, tracking biodiversity, and optimizing drone-based restoration—using geospatial AI and computer vision.
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