AI Prompt Engineer

0 years

0 Lacs

Posted:9 hours ago| Platform: Linkedin logo

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Work Mode

Remote

Job Type

Full Time

Job Description

💡 AI Prompt Engineer – Job Description


Position: AI Prompt Engineer

Location: Remote

Department: AI & Machine Learning


📌 Role Overview


As an AI Prompt Engineer at Zanta.Tech, you will design and optimize input queries (prompts) to guide AI systems in generating accurate, relevant, and contextually appropriate outputs. Your expertise will enhance user interactions with AI models, ensuring high-quality responses across various applications.


🔧 Key Responsibilities


Prompt Design & Optimization: Craft and refine prompts to elicit desired responses from AI models, focusing on clarity, specificity, and context.


Model Training Support: Collaborate with data scientists and engineers to train AI models using well-structured prompts, improving model accuracy and performance.


Performance Monitoring: Analyze AI outputs to identify areas for improvement in prompt effectiveness and make necessary adjustments.


Cross-Functional Collaboration: Work closely with product teams, UX/UI designers, and developers to integrate optimized prompts into applications and platforms.


Documentation & Best Practices: Maintain comprehensive documentation of prompt designs and establish best practices for prompt engineering within the organization.


🎓 Qualifications & Skills


Education: Bachelor's degree in Linguistics, Computer Science, Artificial Intelligence, or a related field.


Technical Skills: Familiarity with AI/ML concepts and tools; experience with programming languages like Python is a plus.


Analytical Skills: Strong problem-solving abilities and attention to detail in analyzing AI outputs.


Communication Skills: Ability to articulate complex ideas clearly and collaborate effectively with interdisciplinary teams.


🌱 Preferred Experience


Experience in designing prompts for large language models (e.g., GPT, BERT) and understanding their behavior.


Knowledge of natural language processing (NLP) techniques and AI model fine-tuning.


Background in user experience (UX) design or human-computer interaction is advantageous.

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