AI Engineer

• Partner with business stakeholders to identify manual, repetitive or inefficient processes suited to automation or AI
• Design, build and maintain automations and integrations using Microsoft Power Platform (Power Automate, Power Apps, Copilot Studio)
• Develop and support AI-enabled workflows and AI agents
• Build integrations across business systems using APIs, connectors and scripting
• Prototype, test and validate solutions before moving them into production
• Document, test and maintain implemented automations and AI solutions
• Monitor deployed solutions for performance, cost and reliability, escalating issues as required
• Apply responsible AI practices in line with Chrysos's AI governance framework
• Evaluate emerging automation and AI platforms and contribute to future adoption
• Work with teams across the business to turn pain points into workable technical solutions
• Explain solution options and trade-offs to non-technical stakeholders
• Collaborate with Infrastructure & Cloud Engineering on integration, security and deployment
• Contribute to AI Guild initiatives, including use case intake, governance and knowledge sharing
• Provide basic training and ongoing support to end users

Automation and AI Engineer

• Build tools, platforms and best practices that help Xero engineers work with AI
• Own solutions end to end from experimentation through production
• Create run books, monitoring and rollback plans for reliable production operation
• Help shape the technical direction of an internal skills marketplace and AI distribution platform
• Build an internal skills marketplace and distribution platform for AI tools across Xero's engineering organisation
• Develop guidance and best practices for authoring agent skills, including remediation-task agents
• Collaborate directly with AWS, Microsoft and GitHub on early-access technology
• Guide and challenge a team early in its AI journey
• Work within the AI Enablement team in Engineering Experience across distributed locations

Senior Engineer – AI Agentic

• Create realistic, challenging coding tasks involving feature implementation and software bug fixes.
• Design tasks that reflect real-world backend engineering scenarios.
• Develop deterministic verifiers that accept reasonable valid solutions and reject incorrect outputs.
• Work with backend technologies such as Java, Node.js, Go, Rust, TypeScript, Python, C++, or similar languages.
• Evaluate code for correctness, reliability, maintainability, and performance.
• Incorporate automated testing and clear validation criteria into coding tasks.
• Identify and account for edge cases, performance bottlenecks, and potential implementation errors.
• Apply secure coding principles and cybersecurity considerations where relevant.
• Refine tasks and verification logic based on project requirements and quality standards.

Backend Software Engineer – AI Training Projects

• Serve as lead technical point of contact across multiple concurrent customer engagements
• Build enterprise architectures and roadmaps incorporating RAG, AI agents, and automated workflows
• Evaluate clients' existing architecture and AI/LLM preparedness and identify gaps to the desired state
• Align business strategy, IT roadmaps, and delivery teams building AI-powered features
• Define governance models covering data privacy, consent, access controls, and responsible-AI guardrails
• Recommend measurement frameworks for AI output quality, accuracy, cost, and latency
• Advise on AI security architecture, including prompt-injection defenses
• Document reference architectures for RAG, agent orchestration, and LLM integration patterns
• Engage marketers, analysts, developers, data scientists, and C-level executives through workshops and deliverables
• Provide enablement and mentoring on product features and AI optimization
• Translate LLM architecture concepts into technical writing and executive presentations
• Manage expectations, scope, and timelines across engagements
• Build trusted advisor relationships with enterprise clients

Enterprise AI Architect

• Lead applied AI at Fetch by setting direction, choosing models and approaches, building systems, measuring performance and shipping them into products
• Build Fetch’s pet health intelligence layer to understand longitudinal pet health information and reason over it
• Own model quality end-to-end, including datasets, evaluations, model selection, prompting, retrieval, post-training, fine-tuning, guardrails and monitoring
• Build evaluation systems and benchmarks to identify model failures and measure improvements
• Develop domain-specific AI using proprietary pet health data and veterinary expertise
• Build multimodal understanding across health records, documents, images and eventually video
• Work with vets, compliance and engineering teams to ship reliable and clinically appropriate production AI
• Help shape the next generation of Fetch products and product direction
• Build the foundations of an AI team and establish a core AI capability
• Collaborate with co-founders, engineering, product, data, vets and vet nurses

Lead Applied AI Engineer

• Define and champion the organization's long-term AI vision, strategy, and enterprise adoption roadmap
• Establish AI as a strategic business capability across operations, customer experience, innovation, workforce productivity, and revenue growth
• Develop enterprise-wide AI transformation frameworks and multi-year capability roadmaps
• Identify emerging AI trends, industry disruptions, and technology opportunities
• Drive executive alignment around AI investments, priorities, and transformation programs
• Design architectural blueprints, reference architectures, standards, design patterns, and governance frameworks
• Ensure interoperability between AI, enterprise applications, cloud platforms, data ecosystems, and operational technologies
• Lead architecture reviews, governance forums, and executive design authorities
• Define AI governance frameworks covering ethical, regulatory, legal, security, privacy, and compliance requirements
• Establish AI operating policies, controls, accountability structures, and model governance practices
• Lead AI risk assessment methodologies and assurance frameworks
• Identify, prioritize, and integrate high-value AI use cases into business processes and decision-making workflows
• Establish business value measurement frameworks and KPIs for AI initiatives
• Build investment cases and executive proposals for major AI transformation programs
• Serve as a trusted advisor to Board members, C-suite executives, and senior business leaders
• Facilitate executive workshops and innovation forums
• Lead AI innovation programs, PoCs, pilot programs, and innovation accelerators
• Establish partnerships with technology vendors, research institutions, startups, academia, and industry bodies
• Represent the organization at industry conferences and executive forums
• Publish strategic viewpoints, white papers, and innovation frameworks
• Monitor global technology trends and evaluate emerging AI opportunities