Key Responsibilities

· Design, develop, and maintain prompt frameworks, including system prompts, few-shot examples, role-based prompts, and reasoning workflows for production-grade LLM applications.

· Build and manage automated evaluation frameworks to measure model performance, accuracy, latency, and regression across releases.

· Conduct structured A/B testing across prompt variations, model versions, and configuration settings to optimize task-specific outcomes.

· Convert product requirements and edge-case scenarios into effective prompt instructions, personas, constraints, and guardrails.

· Partner with ML engineers and product teams to determine when prompt engineering is sufficient versus when fine-tuning, RAG, or other AI architectures are required.

· Create and maintain a centralized prompt repository with version control, documentation, and performance benchmarks for organizational reuse.

· Lead red-teaming and adversarial testing exercises to identify jailbreak risks, hallucinations, and model vulnerabilities.

· Define evaluation criteria, annotation guidelines, and quality standards to ensure consistency, safety, and reliability of AI-generated outputs.

· Mentor engineers and stakeholders on prompt engineering best practices, evaluation methodologies, and the capabilities and limitations of modern LLMs.

· Present prompt strategies, benchmark results, and trade-off analyses to product, engineering, and leadership teams.

· Apply advanced prompting techniques, including chain-of-thought, zero-shot, few-shot, and role-based prompting.

· Drive prompt testing,



evaluation, benchmarking, and continuous optimization efforts.

· Improve AI response quality through systematic assessment, tuning, and refinement.

· Manage context handling and prompt orchestration for complex AI workflows.

Technical Skills

· Strong programming and scripting skills in one or more up-to-date programming languages(C#, Python, Javascript).

· Experience building automation, evaluation pipelines, APIs, or AI-powered applications using enterprise-grade development practices.

· Hands-on experience with LLM platforms, prompt engineering, model evaluation, and AI application development.

· Familiarity with prompt orchestration frameworks, vector databases, RAG architectures, and AI agent workflows.

· Understanding of data analysis, experimentation, benchmarking, and performance optimization.

· Experience with version control systems, CI/CD pipelines, and cloud platforms.

· Strong knowledge of REST APIs, JSON, and system integration patterns.

· Ability to collaborate effectively with software engineers, data scientists, and product teams to deliver production-ready AI solutions.

Experience Requirements

· 4-7 years of combined experience in NLP, AI/ML products, software development, technical writing, or related fields.

· At least 2 years of direct, hands-on prompt engineering experience with production LLM applications.

· Proven track record of owning and managing prompt systems end-to-end, from design and implementation through monitoring and optimization in production.

📌 Prompt Engineer (Sydney)
🏢 Renaissance InfoSystems
📍 Sydney