• Partner closely with prospective customers, subject matter experts, and pre-sales team members to turn ambiguous business problems into compelling, production‑inspired prototypes. • Rapidly design and build enterprise-grade MVP (Minimum Viable Product) agentic AI applications, often within days—that clearly demonstrate how AI agents can transform real business processes. • Showcase technical depth, strong UX/UI taste, and modern AI architecture, while remaining pragmatic and fast to deliver.

• Design job-related questions and review AI-generated responses • Collaborate with government agencies to enhance resource protection • Conduct research to develop improved practices focusing on sustainability • Investigate soil and water quality issues to enhance productivity • Flexible work schedule with approximately 5–20 hours per week when assigned to active projects

• Develop and deliver enablement programs that deeply educate sellers on Cresta’s AI Agent product, including capabilities, architecture, deployment models, and differentiation • Translate technical AI concepts into clear, compelling narratives that resonate with executive, operational, and technical buyer personas • Define and reinforce best-practice sales motions for AI Agent opportunities, from use-case discovery and qualification through solution design and close • Enable sellers to identify high-impact AI Agent use cases, map value to customer KPIs, and position outcomes across industries and contact center environments • Collaborate with Product Marketing to create AI Agent–specific enablement assets, including, talk tracks, objection handling, competitive guidance, and persona-based messaging • Ensure sellers can clearly articulate how Cresta’s AI Agent differs from traditional automation, rules-based bots, or generic AI solutions • Define and track success metrics for AI Agent enablement, such as ramp time, AI Agent attach rate, win rate, sales cycle length, and product adoption indicators • Use insights from deal reviews and field feedback to continuously improve enablement programs and content

• Design, test, and refine prompts to optimize LLM outputs for accuracy, relevance, and tone across diverse use cases. • Train and fine-tune LLMs using proprietary datasets and prompt engineering techniques. • Embed AI prompts into applications and software to automate complex or repetitive tasks. • Monitor performance data to optimize outputs and fine-tune strategies. • Support the integration of AI-powered chatbots into team workflows and enterprise platforms. • Monitor AI system performance, analyze output quality, and iterate based on feedback and metrics. • Measure SEO and adjust strategies to improve performance on key terms. • Collaborate with domain subject matter experts, product, engineering, and UX teams to align AI behavior with user expectations and business goals. • Maintain documentation of prompt libraries, model behaviors, and integration strategies.