AI Engineer – GenAI & Agentic AI | Enterprise AI Solutions | Dubai, UAE

Irisstar; United Arab Emirates; On-site; AED 300,000 - 600,000; Full time

Job summary

Irisstar in Dubai is seeking an AI Engineer to design and deploy enterprise GenAI and Agentic AI solutions, collaborating across AI engineering, data platforms, app development, and MLOps. The role centers on production-grade systems using LLMs, RAG, embeddings, and tool-calling workflows.

You will work with Python, SQL, Spark, and cloud AI technologies, while engaging with distributed teams to deliver scalable AI architectures and certifications.

Qualifications

• Hands-on GenAI and Agentic AI engineering experience.
• Expertise in LLMs, RAG, embeddings, prompt/context engineering, and AI evaluation.
• Experience with LangChain, LangGraph, Haystack, n8n, and Microsoft Copilot.
• Experience building multi-agent architectures, tool calling, and AI workflows.
• Strong knowledge of Azure AI Search, Neo4j, Databricks Vector Search, and vector/graph databases.
• Strong proficiency in Python and SQL.
• Experience with Spark, React, JavaScript, and TypeScript.
• Experience with FastAPI or Flask.
• Knowledge of PyTorch or TensorFlow.
• Experience developing APIs and enterprise integrations.
• Strong understanding of data quality, validation, KPIs, and business rules.
• Experience with production AI systems, MLOps/LLMOps, and AI monitoring.
• Ability to collaborate effectively with distributed and offshore teams.
• Strong communication and problem-solving skills.

Responsibilities

• Design, develop, and deploy enterprise GenAI and Agentic AI solutions.
• Build applications using LLMs, RAG, embeddings, and prompt/context engineering.
• Develop and optimize multi-agent architectures and tool-calling workflows.
• Build AI workflows using LangChain, LangGraph, Haystack, n8n, and Microsoft Copilot.
• Develop solutions using Azure AI Search, Neo4j, Databricks Vector Search, and other vector or graph databases.
• Develop APIs and enterprise integrations using FastAPI or Flask.
• Work with Python, SQL, Spark, React, and JavaScript/TypeScript.
• Apply PyTorch or TensorFlow for AI and machine learning solutions.
• Implement AI evaluation, data validation, KPI validation, and business-rule validation.
• Develop and support production AI systems.
• Implement MLOps/LLMOps practices and AI monitoring.
• Collaborate with distributed and offshore technical teams.
• Continuously improve the reliability, scalability, and performance of AI solutions.

Skills

GenAI engineering

Agentic AI

LLMs

RAG

embeddings

prompt engineering

AI evaluation

LangChain

LangGraph

Haystack

n8n

Microsoft Copilot

multi-agent architectures

tool-calling

AI workflows

Azure AI Search

Neo4j

Databricks Vector Search

Python

SQL

Spark

React

JavaScript

TypeScript

FastAPI

Flask

PyTorch

TensorFlow

APIs & integrations

data quality

KPIs

MLOps/LLMOps

AI monitoring

distributed teams

Tools

LangChain

LangGraph

Haystack

n8n

Microsoft Copilot

Job description

Location

Dubai, United Arab Emirates

Job Category

• Information Technology (IT) & Software
• Science & Research
• Engineering & Technical
• Others / Miscellaneous

Job Overview

An AI Engineer opportunity is available in Dubai for a hands-on professional experienced in building and deploying enterprise Generative AI (GenAI) and Agentic AI solutions. The role focuses on developing production-ready AI systems using large language models, retrieval-augmented generation, multi-agent architectures, AI workflows, and enterprise integrations.

The successful candidate will work across AI engineering, data platforms, application development, and MLOps/LLMOps. Strong expertise in Python, SQL, Spark, AI frameworks, vector and graph databases, and cloud AI technologies is required. The position offers opportunities for professional development, advanced AI training and certification, and long-term career growth in enterprise AI engineering.

Key Responsibilities

• Design, develop, and deploy enterprise GenAI and Agentic AI solutions.
• Build applications using LLMs, RAG, embeddings, and prompt/context engineering.
• Develop and optimize multi-agent architectures and tool-calling workflows.
• Build AI workflows using platforms such as LangChain, LangGraph, Haystack, n8n, and Microsoft Copilot.
• Develop solutions using Azure AI Search, Neo4j, Databricks Vector Search, and other vector or graph databases.
• Develop APIs and enterprise integrations using FastAPI or Flask.
• Work with Python, SQL, Spark, React, and JavaScript/TypeScript.
• Apply PyTorch or TensorFlow for AI and machine learning solutions.
• Implement AI evaluation, data validation, KPI validation, and business-rule validation.
• Develop and support production AI systems.
• Implement MLOps/LLMOps practices and AI monitoring.
• Collaborate with distributed and offshore technical teams.
• Continuously improve the reliability, scalability, and performance of AI solutions.

Requirements & Qualifications

• Strong hands-on experience in Generative AI and Agentic AI engineering.
• Expertise in LLMs, RAG, embeddings, prompt/context engineering, and AI evaluation.
• Experience with LangChain, LangGraph, Haystack, n8n, and Microsoft Copilot.
• Experience building multi-agent architectures, tool calling, and AI workflows.
• Strong knowledge of Azure AI Search, Neo4j, Databricks Vector Search, and vector/graph databases.
• Strong proficiency in Python and SQL.
• Experience with Spark, React, JavaScript, and TypeScript.
• Experience with FastAPI or Flask.
• Knowledge of PyTorch or TensorFlow.
• Experience developing APIs and enterprise integrations.
• Strong understanding of data quality, validation, KPIs, and business rules.
• Experience with production AI systems, MLOps/LLMOps, and AI monitoring.
• Ability to collaborate effectively with distributed and offshore teams.
• Strong communication and problem-solving skills.

Salary, Benefits & Career Growth

Salary and compensation details were not provided by the employer and therefore are not listed.

The role offers opportunities for:

• Advanced professional development in GenAI and Agentic AI.
• Training and upskilling in emerging AI technologies.
• Exposure to enterprise AI architecture and production systems.
• MLOps and LLMOps experience.
• AI engineering and cloud technology certification opportunities.
• Career progression in enterprise AI, machine learning, and AI engineering.