About This Opportunity

We are looking for a Lead 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

• Lead end-to-end delivery of AI/ML and GenAI solutions for enterprise customers across industries such as financial services.
• Own delivery execution across multiple AI workstreams, ensuring scope, timelines, and quality objectives are met.
• Manage technical delivery risks, dependencies, and coordination across engineering, data science, and customer teams.
• Partner with customers to translate business problems into technical AI solutions, including model design, architecture, and deployment approach.
• Oversee implementation of predictive ML, GenAI, and agent-based systems, ensuring alignment to business outcomes.
• Guide design and review of ML models (predictive ML, time series, classification, clustering) and GenAI systems (LLMs, SLMs, RAG, prompt engineering, agentic workflows).
• Ensure best practices across the ML lifecycle: data preparation, feature engineering, training, evaluation, deployment, monitoring, and governance.
• Act as the primary technical point of contact for customers, balancing delivery execution and stakeholder management.
• Collaborate with product and engineering teams to build reusable accelerators and reference architectures.

What We Are Looking For

• 5–8+ years of experience in data science, machine learning, AI engineering, or software engineering with enterprise delivery experience.
• Strong hands-on foundation in predictive machine learning, including feature engineering, model development, evaluation, and interpretability.
• Experience building and deploying production AI systems, including APIs, backend services, or ML-powered applications.
• Solid understanding of GenAI systems including LLMs, SLMs, RAG, prompt engineering, evaluation, and agentic workflows.
• Strong Python skills and production engineering practices (testing, CI/CD, debugging, version control, system design).
• Experience with cloud platforms and MLOps/LLMOps concepts (deployment, monitoring, governance).
• Ability to review technical implementations and guide engineering teams on architecture decisions.
• Strong customer-facing communication skills with experience working directly with technical and business stakeholders.

How to Stand Out From the Crowd

• Experience with H2O products (Driverless AI, H2O-3, H2O Wave, h2oGPTe, H2O LLM Studio).
• Prior experience in technical consulting, solutions engineering, or AI delivery roles.
• Experience leading workshops, hackathons, or enterprise AI enablement programmes.
• Exposure to regulated industries (financial services, healthcare, insurance).
• Experience designing or reviewing full-stack AI applications or platforms.
• Contributions to Kaggle, open source, technical writing, or conference speaking.

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.