Job Description
Prompt Design & Optimization: Develop, refine, and test prompts to achieve accurate, reliable, and context-aware outputs from LLMs (e.g., GPT-4/5, Claude, LLaMA, etc.).
Model Evaluation: Conduct experiments to evaluate prompt effectiveness, measure output quality, and track performance against benchmarks (accuracy, efficiency, consistency).
Knowledge Engineering: Build reusable prompt libraries, templates, and frameworks tailored to business workflows and domain-specific use cases.
Cross-Functional Collaboration: Partner with product managers, data scientists, engineers, and designers to integrate LLM capabilities into products and services.
Documentation & Best Practices: Establish guidelines for prompt engineering, including style, structure, safety, and ethical use.
AI Governance: Work with legal, compliance, and security teams to ensure responsible use of AI and alignment with company policies.
Continuous Improvement: Stay current with advances in generative AI, fine-tuning techniques, and evaluation methodologies.
