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PROMPT ENGINEER

Vrinda International
Posted 10 days ago, valid for 12 days
Location

India, Ellis County 75125, TX

Salary

Competitive

Contract type

Full Time

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Sonic Summary

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  • The company is hiring a Prompt Engineer for a remote position with a budget of ₹1.8 LPM.
  • Candidates must have over 9 years of experience in relevant AI/LLM projects and hold a formal Prompt Engineering certification.
  • The role involves designing, testing, and optimizing prompts for LLM-based applications to enhance AI output quality.
  • Responsibilities include developing prompt templates, running experiments, and collaborating with AI/Engineering teams.
  • The initial contract duration is 3 months, with working hours from 10 AM to 7 PM IST.

? HIRING | PROMPT ENGINEER ?

? Remote | ? 1 Position
? Experience: 9+ Years
? Budget: ₹1.8 LPM
? Contract | ⏳ Initial Duration: 3 Months
? Working Hours: 10 AM–7 PM IST

? ROLE OVERVIEW
Looking for an experienced Prompt Engineer with strong hands-on expertise in designing, testing, evaluating and optimizing prompts for LLM-based applications. The role focuses on improving AI output quality, reliability, consistency and efficiency through structured experimentation.

? MANDATORY
• 9+ years overall experience with relevant AI/LLM projects
• Formal Prompt Engineering Certification – Mandatory
• Hands-on LLM prompt engineering experience
• Prompt design, optimization & evaluation
• Zero-shot & few-shot prompting
• Prompt chaining & RAG
• LLM evaluation methodologies
• Context management, tokenization & context windows
• Identify/mitigate hallucinations, inconsistency & prompt injection
• Strong analytical and experimental mindset
? RESPONSIBILITIES
• Design and optimize prompts for AI use cases
• Develop reusable prompt templates/frameworks
• Run structured experiments and compare prompt variants
• Define evaluation metrics and analyze LLM responses
• Document experiments, results and recommendations
• Work with AI/Engineering teams to integrate optimized prompts
• Deliver measurable outcomes supporting go/no-go decisions





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