3 days ago
Sydney, AustraliaSenior
Responsibilities
- Design and build agentic AI systems and multi-agent frameworks for complex enterprise workflows.
- Develop and deploy LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool use.
- Implement model distillation, synthetic data generation, PEFT-QLORA fine-tuning, guardrails, evaluation frameworks, and responsible AI controls.
- Own the AI application lifecycle from problem framing and data exploration through model development, API integration, production deployment, and monitoring.
- Build scalable backend services, REST APIs, ML pipelines, and LLMOps infrastructure.
- Integrate AI models into cloud, on-premises, and hybrid customer environments.
- Work directly with customer data scientists, engineers, and business stakeholders to translate business problems into AI solutions.
- Contribute to pre-sales and proof-of-concept engagements and communicate technical designs and outcomes to varied audiences.
- Collaborate with Program Managers, Solution Engineers, Kaggle Grandmasters, ML engineers, and domain experts.
Requirements
- At least 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, or fine-tuned models.
- Strong Python engineering skills.
- Experience with PyTorch, TensorFlow, scikit-learn, and LLM tooling such as LangChain or LlamaIndex, or equivalent tools.
- Experience deploying models and AI services in AWS, Azure, GCP, on-premises environments, or Kubernetes.
- Understanding of prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Classical machine-learning knowledge and the ability to select appropriate approaches for different problems.
- Backend development experience with REST APIs, Docker, Kubernetes, and CI/CD pipelines.
- Strong problem-solving, communication, and stakeholder collaboration skills.
- Preferred: Kaggle or competitive ML experience; familiarity with TabH2O, TimeH2O, Lightning-JEV, tabular foundation models, AutoML, or enterprise ML platforms.
- Preferred: Experience in financial services, healthcare, regulated-industry AI deployments, or customer-facing field engineering.
Benefits
- Market leader in total rewards.
- Remote-friendly culture and flexible working environment.
- Career growth opportunities and membership in a world-class team.
- The position is based in Sydney, Australia and is tagged as hybrid.
Tech Stack
Categories
About H2O.ai
H2O.ai builds open-source and commercial platforms for machine learning and generative AI, used by enterprises and public-sector teams to develop models and applications on private data. Its portfolio includes H2O-3, Driverless AI, and tools for LLM fine-tuning and deployment, offered via subscriptions and cloud services. Founded in 2012 and headquartered in Mountain View, it is privately held; customers include AT&T, Commonwealth Bank of Australia, Workday, and the NIH.