• Define conversation flows and dialogue architectures to support multi-turn, context-aware, natural dialogs utlizing tools such as Dialogflow CX. • Develop, write, and optimize prompts to guide large-language models (LLMs) or other generative models toward accurate, context-appropriate, safe outputs • Fine-tune and/or adjust models (when applicable), including prompt tuning, fine-tuning, or parameter optimization, to align model behavior with product and user goals. • Integrate conversational AI with existing backend systems via APIs and data pipelines; ensure seamless data flow, context handling, and retrieval when needed. • Iterate on conversation design through testing: run A/B tests, user simulations, evaluate model outputs (with both automated metrics and human review), debug edge cases, and improve performance over time. • Collaborate closely with product managers, UX / conversation designers, data engineers, and other stakeholders to define requirements, ensure ethical and safe model behavior, and deliver polished user experiences. • Apply MLOps best practices to model versioning, testing, monitoring, and deployment. • Experience with CI/CD pipelines using GitHub Actions, GitOps, GCP-native tools, Azure DevOps, and Jenkins is helpful.