About this role

Design, develop, and optimize prompts and prompt chains for LLM-based applications
Work within Agentic POD architectures to enable seamless interaction between AI agents
Optimize performance of LLMs such as Gemini, Claude, GitHub Copilot, and other foundation models
Develop and implement prompt patterns (few-shot, chain-of-thought, ReAct, tool-augmented prompting)
Build and enhance RAG (Retrieval-Augmented Generation) pipelines for accurate and context-aware responses
Create reusable prompt templates and frameworks for enterprise-scale applications
Improve engineering throughput by leveraging AI-assisted development workflows
Evaluate and fine-tune prompts using LLM evaluation frameworks and metrics (accuracy, latency, cost)
Collaborate with AI engineers, data scientists, and product teams to deliver optimized solutions
Ensure responsible AI practices, bias mitigation, and prompt safety controls
Continuously experiment with new prompting techniques and LLM capabilities

Overall 4 + years with 2-7 years of relevant work experience in Agentic AI Development
B.Tech., M.Tech. or MCA degree from a reputed university

Prompt Engineer

Key skills