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 modern 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.