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AI NLP Engineer

Remote · New Zealand Full-time

Position AI NLP Engineer Jobs Job Description Collaborate on projects that improve generative AI models' abilities to handle specialized legal queries and complex reasoning. Generate training prompts, define evaluation criteria, and correct AI model responses based on your domain-specific legal knowledge. The AI Legal Trainer generates prompts, defines scoring criteria, and corrects AI model responses in legal domains. They contribute domain expertise to help improve generative AI models’ capacity for specialized reasoning. AI Prompt Engineers build custom workflow configurations and extend Magical’s capabilities using AI prompting and internal tools. They work closely with customers to deploy, integrate, and optimize the AI automation platform for specific use cases. The specialist will design structured evaluation scenarios for LLM-based agents, creating test cases that simulate human-performed tasks and establishing gold-standard behavior for comparison. They will annotate tasks, work with developers to refine clarity, and iteratively review and adapt scenarios based on agent outputs. The Prompt Engineer optimizes prompts for generative AI models within NICE's Customer Service Automation applications and collaborates with developers, architects, and product managers to deliver high-quality customer experiences. Responsibilities include diagnosing and resolving prompt-related issues, executing tests for LLM-based systems, updating production prompts, and consulting with teams to address novel requirements. The Acting Teacher - Voice AI Trainer collaborates with AI developers to improve voice recognition and synthesis through expert coaching on accents and regional dialects. They lead voice coaching sessions, refine AI-generated outputs, develop training materials, and document project progress. The Conversational AI Engineer will lead the development of an AI concierge service by designing, optimizing, and implementing conversational flows and integrating advanced language models such as LLMs. They will collaborate with backend teams, manage AI-related data, analyze and improve AI system performance, and maintain thorough documentation. Location Maybe global

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