Prompt Engineer

Hire Rightt - Executive Search & HR Advisory; Dubai; On-site; AED 180,000 - 300,000; Full time

Job summary

Hire Rightt - Executive Search & HR Advisory in Dubai seeks a Prompt Engineer with 5+ years of hands-on experience in production AI. You will design and optimize prompts for LLM applications, build AI agents, and develop robust evaluation frameworks.

This is a hands-on engineering role focused on prompt design, RAG workflows, JSON Schema, and secure agent design, with collaboration across product and engineering to deploy production-grade prompts and tools.

Qualifications

• 5+ years hands-on experience with LLMs and GenAI in production.

Responsibilities

• Design, test, and optimize prompts for LLM applications and AI agents.
• Build system prompts, guardrails, evaluation prompts, and reusable prompt templates.
• Develop and evaluate AI agents using RAG, tool calling, memory, and multi-agent workflows.
• Create synthetic datasets and evaluation benchmarks for LLM applications.
• Build automated evaluation pipelines to measure accuracy, quality, latency, reliability, and safety.
• Analyze failures and improve system behavior.

Skills

Prompt Engineering

LLM workflows

RAG

Tool calling

Memory management

JSON Schema

Python

Tools

LangChain

LangGraph

LlamaIndex

LLM APIs

Job description

Role: Prompt Engineer

Experience: 5+ Years

Dubai Based Role

About the Role

We are looking for a Prompt Engineer to join our AI team and build reliable, production-grade experiences powered by Large Language Models (LLMs).

You will design and optimize prompts, build and evaluate AI agents, improve RAG and tool-calling workflows, and develop evaluation frameworks that ensure AI systems are accurate, safe, consistent, and production-ready.

This is a hands-on engineering role for someone who understands both prompt engineering and the underlying GenAI/LLM application stack.

What You’ll Do

• Design, test, and optimize prompts for LLM applications and AI agents.
• Build system prompts, guardrails, evaluation prompts, and reusable prompt templates.
• Develop and evaluate AI agents using RAG, tool calling, memory, and multi-agent workflows.
• Create synthetic datasets and evaluation benchmarks for LLM applications.
• Build automated evaluation pipelines to measure accuracy, quality, latency, reliability, and safety.
• Analyze LLM and agent failures and continuously improve system behavior.
• Work with AI engineers to integrate prompts and agent workflows into production APIs.
• Translate product and business requirements into precise AI behaviors.
• Implement techniques for hallucination reduction, prompt-injection protection, and secure agent design.
• Work with structured outputs, function calling, and JSON Schema.
• Experiment with MCP, AI gateways, model routing, and different LLM inference approaches.
• Document prompt strategies, evaluation methodologies, and AI engineering best practices.

What We’re Looking For

• 5+ years of hands-on experience with LLMs and Generative AI in production.
• Strong expertise in Prompt Engineering across reasoning, extraction, summarization, classification, and agentic workflows.
• Strong experience building AI agents with RAG, tool calling, and memory.
• Hands-on experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
• Experience with synthetic data generation and LLM evaluation.
• Strong understanding of hallucination mitigation, AI safety, guardrails, and prompt injection risks.
• Experience with structured outputs and JSON Schema.
• Strong Python programming skills and ability to work with AI/ML APIs.
• Experience with evaluation and observability platforms such as LangSmith, DeepEval, Ragas, or similar.
• Understanding of MCP, AI gateways, and model routing.
• Understanding of cloud vs. local LLM inference, including latency, scalability, security, and cost considerations.
• Strong communication and collaboration skills with product and engineering teams.

Nice to Have

• Experience with OpenAI, Anthropic, Gemini, Llama, Mistral, or similar models.
• Experience with vector databases and embedding models.
• Experience building production-grade GenAI APIs.
• Experience with multi-agent architectures and agent orchestration.
• Experience in financial services, trading, Forex, or other regulated environments.