Job Description
/nH2O.ai is on a mission to democratize AI for Good. 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 Agents, SLMs, and solutions on their private data. With a focus on secure, compliant, and infrastructure-flexible Sovereign AI deployments, H2O.ai delivers solutions that align with the highest standards of data privacy and control./nIts 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, Wells Fargo, Bank of America, Workday, Progressive Insurance, and NIH./nFor more information, visit www.h2o.ai. /nAbout This Opportunity /nWe are looking for a Senior 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./nYou 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./n This position is based in Sydney, Australia. What You Will Do/nAgentic AI & LLM Engineering/n/n
- Model distillation and synthetic data generation for fine-tuning an open source model (eg. Gemma). Experience with PEFT-QLORA frameworks /n
- Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers./n




- Develop and deploy LLM-powered applications using techniques including RAG, fine-tuning, prompt engineering, function calling, and tool use./n
- Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety./n
- Stay current with the rapidly evolving agentic AI landscape
- MCP, LLM orchestration frameworks, reasoning models
- and bring the best of it into customer engagements./n/nEnd-to-End AI Application Development/n/n
- Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment./n
- Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows./n
- Integrate AI models into customer environments
- cloud, on-prem, and hybrid
- ensuring performance, stability, and maintainability at scale./n
- Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production./n/nCustomer Engagement & Delivery/n/n
- Work directly with customer data scientists, engineers, and business stakeholders to translate real-world problems into AI solutions./n
- Contribute to pre-sales and proof-of-concept engagements
- building fast, credible demonstrations that win technical trust./n
- Communicate clearly across audiences:



from detailed technical design reviews with engineering teams to outcome-focused updates for business stakeholders./n
- Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high-quality solutions./n/n/n What We Are Looking For/nExperience & Background/n/n
- 3+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment./n
- Demonstrable experience building LLM-powered applications
- RAG pipelines, agentic workflows, fine-tuned models, or similar./n
- Solid Python engineering skills; experience with ML frameworks (PyTorch, Tensor Flow, scikit-learn) and LLM tooling (Lang Chain, Llama Index, or equivalent)./n
- Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes)./n/nSkills & Capabilities/n/n
- Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps./n
- Solid grounding in classical ML
- able to select the right tool for the problem, not just default to the latest LLM./n
- Backend development
skills:
REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications./n
- Strong problem-solving instincts
- comfortable with ambiguity, able to move fast without sacrificing engineering quality./n
- Clear communicator who can explain complex AI systems to non-technical stakeholders without oversimplifying./n/nHow to Stand Out From the Crowd/n/n
- Kaggle or competitive ML experience./n
- Familiarity with the latest H2O.ai products such as TabH2O, TimeH2O and Lightning-JEV/n
- Experience in financial services, healthcare, or other regulated industry AI dep

📌 Senior AI Engineer (Sydney)
🏢 trabajo au
📍 Sydney