• Go to the field: talk to users, shadow their workflows, capture the as‑is → goals & constraints. • Define hypotheses & KPIs (precision/recall, FPR, TAT, coverage, cost/decision) and turn them into experiment plans. • Design decision flows that mix LLMs, retrieval/RAG, classical ML, and lightweight rules; ensure explainability and auditability. • Build quick prototypes (notebook → lightweight service/API) and measure their impact on real data. • Create evaluation sets and scoring rubrics (offline + side‑by‑side + sanity checks + guardrails). • Present findings & recommendations directly to decision‑makers; propose rollout (pilot → production‑lite → scale). • Lead innovation processes across the company; test, promote solutions and mentor others with new AI technologies.

Title: Senior AI Engineer Location: Sydney Australia Job Description: Founded in 2012, H2O.ai is on a mission to democratize AI. As the world's leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built GenAI applications on their private data. With a focus on Sovereign AI-secure, compliant, and infrastructure-flexible deployments-H2O.ai delivers solutions that align with the highest standards of data privacy and control. Our open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500. H2O.ai powers AI transformation for companies like AT&T, Commonwealth Bank of Australia, Chipotle, Workday, Progressive Insurance, and NIH. H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst & Young (EY), Snowflake, AWS, Google Cloud Platform (GCP), VAST Data and MinIO. H2O.ai's AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation. With a vibrant community of 2 million data scientists worldwide, H2O.ai aims to co-create valuable AI applications for all users. H2O.ai has raised 256 million from investors, including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life. About This Opportunity We are looking for an AI Engineer who builds things that matter. You will design and ship end-to-end AI solutions for some of APAC's most complex enterprise problems - spanning agentic AI systems, LLM applications, and production ML pipelines. This is a hands-on engineering role embedded within a customer-facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one. You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos - into production, into workflows, and into measurable business outcomes. This position is based in Sydney, Australia. What You Will Do Agentic AI & LLM Engineering - Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers. - Develop and deploy LLM-powered applications using techniques including RAG, fine-tuning, prompt engineering, function calling, and tool use. - Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety. - Stay current with the rapidly evolving agentic AI landscape - MCP, LLM orchestration frameworks, reasoning models - and bring the best of it into customer engagements. End-to-End AI Application Development - Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment. - Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows. - Integrate AI models into customer environments - cloud, on-prem, and hybrid - ensuring performance, stability, and maintainability at scale. - Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production. Customer Engagement & Delivery - Work directly with customer data scientists, engineers, and business stakeholders to translate real-world problems into AI solutions. - Contribute to pre-sales and proof-of-concept engagements - building fast, credible demonstrations that win technical trust. - Communicate clearly across audiences: from detailed technical design reviews with engineering teams to outcome-focused updates for business stakeholders. - Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high-quality solutions. What We Are Looking For Experience & Background - 3+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment. - Demonstrable experience building LLM-powered applications - RAG pipelines, agentic workflows, fine-tuned models, or similar. - Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent). - Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes). Skills & Capabilities - Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps. - Solid grounding in classical ML - able to select the right tool for the problem, not just default to the latest LLM. - Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications. - Strong problem-solving instincts - comfortable with ambiguity, able to move fast without sacrificing engineering quality. - Clear communicator who can explain complex AI systems to non-technical stakeholders without oversimplifying. How to Stand Out From the Crowd - Kaggle or competitive ML experience. - Familiarity with H2O.ai products, Wave, or H2O Document AI. - Experience in financial services, healthcare, or other regulated industry AI deployments. - Exposure to tabular foundation models, AutoML, or enterprise ML platforms. - Prior experience in a customer-facing or field engineering role. Why H2O.ai - Market Leader in Total Rewards - Remote-Friendly Culture - Flexible working environment - Be part of a world-class team - Career Growth H2O.ai is committed to creating a diverse and inclusive culture. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis. H2O.ai is an innovative AI cloud platform company, leading the mission to democratize AI for everyone. Thousands of organizations from all over the world have used our cutting-edge technology across a variety of industries. We've made it easy for people at all levels to generate breakthrough solutions to complex business problems and advance the discovery of new ideas and revenue streams. We push the boundaries of what is possible with artificial intelligence. H2O.ai employs the world's top Kaggle Grandmasters, the community of best-in-the-world machine learning practitioners and data scientists. A strong AI for Good ethos and responsible AI drive the company's purpose. #LI-Hybrid

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains • Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems • Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics • Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes • Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact • Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics • Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes • Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes • Drive adoption and usage of shared data assets across business units • Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization) • Ensure solutions are aligned to real-world workflows and deliver tangible outcomes • Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships • Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases • Ensure alignment of definitions across domains to reduce fragmentation and duplication • Support both structured and unstructured data integration, enabling downstream AI and analytics applications • Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships • Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions • Own the end-to-end product lifecycle from discovery and definition to delivery and iteration • Define product requirements, success metrics, and release plans • Continuously refine the semantic data product based on user feedback and evolving business needs • Define critical metrics to measure adoption, usability, and impact of the semantic data product • Supervise usage and continuously improve accessibility and value delivery • Evangelize the value of connected semantic data across the organization • Partner with data governance teams to establish standards, definitions, and data quality expectations • Promote consistency, reusability, and scalability of data assets across the enterprise

• Deliver AI powered solutions driving both internal efficiencies and new customer value through data insights • AI/ML Solution Implementation: Work closely with business leaders and data scientists to understand user needs and transition AI/ML prototypes into production-ready solutions • Full-Scale Model Deployment: Design, develop, and deploy scalable machine learning models and algorithms to solve complex business problems. • Data Integration: Ensure efficient collection and integration of data from diverse sources for training and validation of AI models. • System Integration: Collaborate with tech teams to integrate AI/ML solutions seamlessly into existing systems and enhance data collection processes. • Performance Optimization: Continuously monitor and optimize model performance to ensure responsiveness and scalability. • Code Quality and Maintenance: Write clean, maintainable, and well-documented code, ensuring best practices in software development. • Process Improvement: Leverage AI/ML to identify opportunities for process improvement and contribute to strategic projects aimed at enhancing business performance and competitiveness. • Change Management and Adoption: Ensure successful adoption of AI/ML solutions by managing organizational change, assisting with user training, and conducting follow-up post implementation to support users and address any issues.