Data Scientist | LLM Engineer | Manager/Senior Manager
On-site · Sydney, New South Wales, Australia
Job Summary
Design, develop, and deploy production-grade GenAI solutions and LLM-powered applications for PwC's firmwide business across tax, legal, consulting, assurance, and financial advisory lines of service. Build and optimise RAG systems, agentic workflows, and multi-agent architectures to solve complex business problems at scale. Develop and fine-tune LLMs, implement advanced prompt engineering strategies, and create robust evaluation frameworks for model performance monitoring. Architect data pipelines to extract, cleanse, and normalise data from unstructured and semi-structured sources, integrating with vector databases and embedding models. Produce clean, maintainable, production-ready code deployed on Azure cloud infrastructure, following MLOps best practices and contributing to the full software development lifecycle. Mentor junior data scientists in GenAI technologies and actively participate in LLM and AI communities within the firm.
Required Qualifications
- Industrial experience deploying LLM solutions in production environments with demonstrated impact on business outcomes
- Strong expertise in GenAI/LLM technologies including GPT-4, Claude, Llama, and other foundation models, with hands-on experience in model selection, fine-tuning, and adaptation
- Proven experience building RAG systems with vector databases and implementing hybrid search strategies
- Experience designing and deploying agentic systems and multi-agent frameworks for complex task orchestration
- Strong Python development skills with modern LLM frameworks and deep understanding of prompt engineering techniques
- Hands-on experience with LLM observability and evaluation tools
- Deep understanding of microservices architecture, containerisation, and Azure cloud services (Azure OpenAI, Azure ML, Azure Functions)
- Experience with MLOps practices, CI/CD pipelines for ML systems, source code management (Git), and agile software development methodologies
- Excellent critical thinking, problem-solving abilities, and communication skills with the ability to translate complex technical concepts for business stakeholders
- Plus: Traditional ML/DL experience, knowledge of model quantisation and optimisation techniques, Typescript development skills, and familiarity with guardrails and AI safety frameworks
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