Prompt Engineer

Hyderabad Hybrid

Exp- 2+ yrs

About the Role

We are looking for a talented and detail-oriented Prompt Engineer to join our growing AI team. In this role, you will be responsible for crafting, evaluating, and optimizing prompts that drive the behavior of LLM-powered applications and intelligent agent systems. You will play a pivotal role in bridging the gap between business requirements and AI-driven outcomes, ensuring that our systems perform accurately, safely, and reliably in production.

Key Responsibilities

● Design, test, and systematically optimize prompts for LLM-powered applications and AI agent pipelines.

● Create and continuously refine system prompts, guardrails, evaluation prompts, and multi-agent interaction frameworks.

● Collaborate closely with AI backend engineers to seamlessly integrate prompts into production APIs and agent workflows.

● Develop comprehensive prompt evaluation datasets, scoring methodologies, and automated testing pipelines to ensure consistent quality.

● Experiment with Retrieval-Augmented Generation (RAG) pipelines, memory systems, tool-calling mechanisms, and agent orchestration strategies.

● Analyze agent failures and iterate on prompt designs using structured evaluation frameworks and real-world production feedback.

● Partner with product and business stakeholders to translate functional requirements into precise, well-defined AI behaviors.

● Document prompting strategies, established best practices, and maintain a library of reusable prompt templates for organizational use.

Skills & Memory Required Qualifications





● 2+ years of hands-on experience working with Large Language Models (LLMs) and Generative AI in production environments.

● Strong command of prompt engineering techniques, including reasoning, information extraction, summarization, and agentic workflow design.

● Proven experience with synthetic dataset generation and AI evaluation methodologies.

● Familiarity with Model Context Protocols (MCP), AI gateways, and model routing strategies.

● Solid understanding of AI safety principles, prompt injection risks, and secure agent design practices.

● Experience working with AI frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.

● Demonstrated experience in building and evaluating AI agents utilizing tool-calling, RAG architectures, and memory systems.

● Strong understanding of hallucination mitigation strategies and their practical application in production systems.

● Experience with structured output generation and JSON schema design for LLM responses.

● Proven ability to design and implement evaluation pipelines assessing LLM quality, accuracy, latency, and safety.

● Comfortable working with Python and LangChain, with a strong ability to collaborate effectively with AI engineers.

● Hands-on experience with AI monitoring and evaluation tools such as LangSmith, DeepEval, Ragas, or similar platforms.

● Explicit understanding of the distinctions between local and cloud-based LLM inferencing, and the implications of each in production contexts.

Interested candidates can share their updated CV at [HIDDEN TEXT]

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