A leading Fintech / Banking organisation in Dubai is seeking an experienced Prompt Engineer to craft, evaluate, and optimise prompts that drive the behaviour of LLM-powered applications and intelligent agent systems — offering a monthly salary of AED 25,000. This pivotal technical role bridges the gap between business requirements and AI-driven outcomes — ensuring systems perform accurately, safely, and reliably in production — requiring 3+ years of hands-on LLM and Generative AI production experience, deep expertise in prompt engineering, RAG architectures, agentic workflow design, AI safety, hallucination mitigation, Model Context Protocol (MCP), and proficiency with evaluation platforms including LangSmith, DeepEval, and Ragas.
About This Prompt Engineer Opportunity — Fintech / Banking Dubai
Industry: Fintech / Banking — one of the UAE’s highest-value and most AI-active industries, with complex requirements for accurate, safe, and reliable LLM-powered systems
Core Mission: Craft, evaluate, and optimise prompts and agentic workflows that drive LLM-powered applications to perform accurately, safely, and reliably in production banking environments
AI Scope: Prompt engineering, agentic system design, RAG architectures, tool calling, synthetic dataset generation, LLM evaluation pipelines, and hallucination mitigation
Apply: Send your updated CV to srividhya@hirerightt.com — only shortlisted candidates will be contacted due to high application volume
Why This Prompt Engineer Role Stands Out in Dubai 2026
AED 25,000 Salary: One of Dubai’s most competitive salary packages for a Prompt Engineer — reflecting the scarcity and commercial value of production LLM expertise in the Fintech/Banking sector
Full Production LLM Scope: Prompt design, evaluation pipelines, hallucination mitigation, AI safety, MCP, agentic workflows, RAG — the complete modern LLM engineering mandate
Fintech Banking Domain: The highest-stakes LLM deployment context — where accuracy, safety, and hallucination prevention are not nice-to-haves but operational non-negotiables
Advanced Evaluation Tools: LangSmith, DeepEval, Ragas — proficiency in the UAE’s most in-demand LLM evaluation and monitoring platforms in a single senior role
Position Overview
This Prompt Engineer in Dubai’s Fintech/Banking sector crafts, evaluates, and optimises prompts that drive the behaviour of LLM-powered applications and intelligent agent systems — bridging the gap between business requirements and AI-driven outcomes. The role involves designing prompts for reasoning, information extraction, summarisation, and agentic workflows, generating synthetic datasets for LLM training and evaluation, implementing AI evaluation pipelines measuring quality, accuracy, latency, and safety, working with Model Context Protocol (MCP), AI gateways, and model routing strategies, ensuring AI safety and protection against prompt injection risks, building and evaluating AI agents using tool calling, RAG architectures, and memory systems, applying hallucination mitigation techniques in production, and generating structured outputs using JSON Schema — collaborating closely with AI/ML engineering teams across the organisation.
Why This Role Matters: As Prompt Engineer in a Dubai Fintech/Banking environment, your work determines whether LLM-powered applications can be trusted with the outputs that financial institutions — and their customers — rely on. A poorly crafted prompt in a credit analysis tool produces unreliable recommendations. A prompt injection vulnerability in a banking AI agent exposes sensitive customer data. A hallucination in a financial summary misleads the decision-maker who reads it. Your expertise in prompt design, RAG architecture, evaluation pipelines, AI safety, and hallucination mitigation is what separates Fintech AI systems that are impressive from Fintech AI systems that are actually safe to deploy in production at banking scale.
Key Responsibilities
Prompt Design, Optimisation & Agentic Workflow Engineering
- Craft and optimise prompts for reasoning, information extraction, summarisation, and complex agentic workflow execution across LLM-powered Fintech/Banking applications
- Design and implement agentic workflow architectures — including multi-step agent pipelines with tool calling, memory systems, and action boundaries appropriate for banking-grade AI systems
- Work with Model Context Protocol (MCP), AI gateways, and model routing strategies — managing how prompts, context, and tool access flow through complex AI system architectures
- Generate structured outputs using JSON Schema for LLM responses — ensuring AI system outputs are consistently formatted, parseable, and safe for downstream financial application consumption
RAG Architecture, AI Agents & Memory Systems
- Build and evaluate AI agents using tool calling, RAG (Retrieval-Augmented Generation) architectures, and memory systems — creating agents that retrieve accurate context before generating financial outputs
- Design and implement RAG pipelines tailored to Fintech/Banking domain requirements — ensuring retrieval relevance, context quality, and output accuracy at production scale
- Apply hallucination mitigation techniques in production systems — designing prompts, evaluation, and retrieval strategies that reduce factual errors in LLM-generated financial content
- Evaluate local versus cloud-based LLM inference options — understanding latency, cost, data residency, and performance implications for banking production environments
AI Safety, Evaluation Pipelines & Synthetic Data
- Design and implement LLM evaluation pipelines measuring quality, accuracy, latency, and safety — using LangSmith, DeepEval, Ragas, or equivalent AI monitoring and evaluation platforms
- Assess AI safety risks — including prompt injection attacks, jailbreak attempts, and insecure agent design patterns — implementing guardrails and defensive prompt architectures for financial AI systems
- Generate synthetic datasets for LLM training, fine-tuning, and evaluation — producing high-quality, domain-relevant data that improves LLM performance on Fintech/Banking tasks
- Collaborate effectively with AI/ML engineering teams — bridging prompt engineering expertise with software engineering delivery to ensure LLM applications reach production reliably
Requirements & Qualifications
Essential Requirements
- 3+ years of hands-on experience working with Large Language Models (LLMs) and Generative AI in production environments
- Strong expertise in prompt engineering — reasoning, information extraction, summarisation, and agentic workflow design
- Proven experience in synthetic dataset generation and AI/LLM evaluation methodologies
- Familiarity with Model Context Protocol (MCP), AI gateways, and model routing strategies
- Strong understanding of AI safety, prompt injection risks, and secure agent design principles
- Experience with AI frameworks — LangChain, LangGraph, LlamaIndex, or equivalent
- Hands-on experience building and evaluating AI agents using tool calling, RAG architectures, and memory systems
- Strong understanding of hallucination mitigation and its practical application in production systems
- Experience with structured output generation and JSON Schema for LLM responses
- Proven ability to design and implement LLM evaluation pipelines measuring quality, accuracy, latency, and safety
- Strong Python skills with ability to collaborate with AI/ML engineering teams
- Hands-on experience with AI monitoring and evaluation tools — LangSmith, DeepEval, Ragas, or similar
- Understanding of local versus cloud-based LLM inference and production implications
About Prompt Engineering in Dubai Fintech & Banking — 2026
Prompt Engineering in Fintech and Banking contexts is among the most technically demanding and commercially significant AI specialisms in the UAE in 2026. With financial institutions across Dubai actively deploying LLM-powered applications for customer service, credit analysis, document processing, compliance, and financial insight generation — the engineers who can design accurate, safe, hallucination-resistant, and evaluation-validated prompt systems are commanding premium salaries and central roles in digital transformation programmes. At AED 25,000/month for a Dubai Fintech Prompt Engineer, this opportunity offers competitive market compensation for the expertise combination — production LLMs, RAG, agentic design, MCP, AI safety, and evaluation — that the UAE banking sector most urgently needs in 2026.
Career Excellence: Engineer prompts, agents, and evaluation pipelines for production LLM systems in Dubai Fintech — AED 25,000/month, LangChain, RAG, MCP, LangSmith.
Who Should Apply?
- Production LLM & Prompt Engineers: With 3+ years of real production LLM experience — prompt optimisation, agentic workflow design, RAG implementation, and evaluation pipeline delivery in high-stakes environments
- RAG Architecture & AI Agent Specialists: With hands-on LangChain, LangGraph, or LlamaIndex experience — building tool-calling agents, RAG systems, and memory-augmented LLM applications in production
- LLM Safety & Evaluation Engineers: With LangSmith, DeepEval, or Ragas experience — designing and operating evaluation pipelines that measure production LLM quality, safety, and performance at scale
- Fintech & Banking AI Engineers: With domain understanding of financial data sensitivity, regulatory context, and the hallucination risk tolerances that make financial AI deployment uniquely challenging
- Dubai-Based GenAI Python Engineers: With MCP, model routing, AI safety, JSON Schema, and synthetic data generation experience — ready for a senior Prompt Engineer role at AED 25,000/month in Dubai’s Fintech sector
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