A leading technology organization in Abu Dhabi, UAE is urgently hiring a hands-on Senior Generative AI Engineer / Technical Lead for a full-time, 5-days-onsite role. This is an enterprise-grade GenAI engineering position responsible for designing and delivering production Generative AI solutions across conversational AI, process automation, and mission-critical business applications. The role requires 6–8+ years of AI/software engineering experience with hands-on expertise in Azure OpenAI, RAG pipelines, LangChain, Semantic Kernel, LLM orchestration, Python, vector databases, and Kubernetes. Immediate to 30-day joiners are strongly preferred — and candidates currently outside the UAE who are ready to relocate to Abu Dhabi are welcome to apply.
About the Role — Generative AI Technical Lead, Abu Dhabi
Role: Senior Generative AI Engineer / Technical Lead — Enterprise AI Solutions
Core Stack: Azure OpenAI · Azure AI Studio · LangChain · Semantic Kernel · AutoGen · LlamaIndex
AI Architecture: RAG · Embeddings · Semantic Search · Knowledge-Base Integration · Agentic AI
Infrastructure: Docker · Kubernetes · AKS · CI/CD · REST APIs · Event-Driven Architecture
Industry Advantage: Aviation · Banking · Healthcare · Government · Regulated industry experience a plus
Why This GenAI Technical Lead Role in Abu Dhabi Is a Career-Defining Opportunity
Enterprise GenAI: Design and deliver production-grade Generative AI systems — real business impact, not research prototypes
Technical Leadership: Lead architecture decisions, guide engineers, and own the GenAI delivery end-to-end
Immediate Opportunity: Preferred notice period of immediate to 30 days — your skills are urgently needed right now
Tax-Free Abu Dhabi: Highly competitive GenAI engineer salary — zero personal income tax in the UAE
Position Overview
This Senior Generative AI Engineer / Technical Lead role in Abu Dhabi is a full-time, onsite engineering leadership position at the center of enterprise AI transformation. You will design and deliver production Generative AI solutions using Azure OpenAI and Azure AI Studio, build and optimize RAG pipelines with vector database backends, implement multi-agent and agentic AI workflows using LangChain, Semantic Kernel, AutoGen, and LlamaIndex, and deploy scalable AI systems on Kubernetes and AKS using Docker and CI/CD pipelines. Beyond pure technical delivery, you will lead technical discussions, guide junior engineers, influence architecture decisions, and ensure GenAI solutions are reliable, secure, and production-ready for enterprise clients in regulated industries across the UAE. Candidates currently outside the UAE who are willing and ready to relocate to Abu Dhabi are explicitly welcomed to apply.
Why This Is the GenAI Engineering Career Opportunity of 2026 in Abu Dhabi: Senior Generative AI Engineers who combine Azure OpenAI production delivery experience with RAG architecture expertise, LangChain/Semantic Kernel framework mastery, agentic AI workflow design, and the technical leadership ability to guide engineering teams are among the rarest and most commercially valuable technology professionals in the entire UAE in 2026. Abu Dhabi’s accelerating enterprise AI transformation across government, aviation, healthcare, and banking creates exceptional demand for this exact skillset — and this role puts you at the center of that transformation.
Key Responsibilities
Generative AI Solution Design & Enterprise Architecture
- Design and architect enterprise-grade Generative AI solutions that address real business challenges across conversational AI, intelligent process automation, document intelligence, and customer-facing AI applications
- Lead the end-to-end technical delivery of GenAI projects — from initial solution architecture and proof-of-concept development through integration, testing, deployment, and production monitoring
- Apply hands-on expertise with Azure OpenAI and Azure AI Studio to build, fine-tune, and optimize LLM-powered applications that deliver measurable performance and reliability at enterprise scale
- Influence and own architecture decisions for GenAI systems — evaluating framework options, infrastructure trade-offs, and integration approaches to select the best solution for each enterprise use case
RAG Pipelines, Embeddings & Knowledge-Base Integration
- Build and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines — implementing document ingestion, chunking strategies, embedding generation, semantic search, and LLM response synthesis for production enterprise applications
- Design and implement knowledge-base integration architectures — connecting enterprise data sources, document repositories, and structured databases with LLM-powered retrieval systems for accurate, grounded AI responses
- Configure and optimize vector databases — including Azure AI Search, Pinecone, Weaviate, and pgvector — for high-performance semantic search, embedding storage, and nearest-neighbor retrieval within RAG architectures
- Apply expertise in embedding models, chunking strategies, re-ranking techniques, and hybrid search approaches to continuously improve RAG pipeline accuracy, relevance, and retrieval performance
LLM Orchestration — LangChain, Semantic Kernel & AutoGen
- Implement production LLM orchestration solutions using LangChain, Semantic Kernel, AutoGen, and LlamaIndex — building chains, agents, memory systems, and tool integrations that power enterprise-grade AI applications
- Design, implement, and optimize agentic AI and multi-agent workflow architectures — enabling autonomous AI systems to plan, execute, and coordinate multi-step tasks across complex enterprise process environments
- Develop robust prompt engineering strategies, structured output patterns, and error-handling frameworks that ensure consistent, reliable, and production-safe LLM behavior across diverse enterprise use cases
- Integrate GenAI solutions with enterprise systems via REST APIs, event-driven architectures, webhooks, and enterprise integration platforms — ensuring seamless data flow and reliable operational performance
Cloud Infrastructure, Kubernetes & CI/CD Delivery
- Deploy, manage, and scale GenAI applications on Azure Kubernetes Service (AKS) and Docker container infrastructure — ensuring production reliability, horizontal scalability, and cost-efficient cloud resource utilization
- Design and maintain CI/CD pipelines for GenAI applications — enabling automated testing, model versioning, safe deployment rollouts, and monitoring integrations that support continuous improvement of production AI systems
- Apply strong knowledge of REST APIs, event-driven architecture patterns, and enterprise integration standards to connect GenAI solutions reliably across existing organizational technology landscapes
Technical Leadership & Team Development
- Lead technical discussions and architecture reviews — providing clear, well-reasoned engineering guidance that aligns GenAI solution design with business objectives, security requirements, and performance standards
- Guide and mentor junior and mid-level AI engineers — helping them develop production GenAI skills, improve code quality, and build confidence in delivering complex LLM-powered systems
- Communicate technical concepts, architecture decisions, and progress clearly to both engineering peers and non-technical business stakeholders — ensuring aligned understanding across all project participants
Qualifications & Requirements
Experience Requirements
- 6–8+ years of professional experience in software engineering or AI engineering — with at least 3–4 years of hands-on experience delivering production Generative AI or advanced ML solutions in commercial environments
- Proven production delivery track record — not just research or prototype work, but enterprise AI systems that handle real user traffic, production data, and business-critical workflows at scale
- Experience in aviation, banking, healthcare, government, or other regulated industries is a strongly valued advantage — demonstrating understanding of compliance, security, and governance requirements in sensitive AI deployments
Technical Skills
- Strong Python programming skills — writing clean, production-quality, well-tested code for AI application backends, data pipelines, and LLM integration layers
- Hands-on Azure OpenAI and Azure AI Studio experience — deploying, configuring, and operationalizing LLM models in Azure enterprise environments
- Practical RAG pipeline development experience — embeddings, chunking, vector store configuration, semantic search, and grounded LLM response generation
- LangChain, Semantic Kernel, AutoGen, or LlamaIndex framework experience — building orchestration layers, agents, and multi-step AI workflows for enterprise applications
- Docker, Kubernetes, and AKS deployment experience — containerizing and scaling AI applications in cloud-native production infrastructure
- Understanding of agentic AI principles and multi-agent workflow design — enabling autonomous AI task execution in complex enterprise process environments
About Generative AI Engineering in Abu Dhabi 2026
Abu Dhabi is emerging as one of the most strategically significant and rapidly growing Generative AI deployment markets in the world — driven by massive government investment in AI infrastructure, ADNOC’s digital transformation agenda, Abu Dhabi’s healthcare and aviation modernization programs, and the UAE’s national commitment to AI leadership under Vision 2031. Senior Generative AI Engineers who can design, build, and operationalize enterprise-grade RAG architectures, agentic AI systems, and LLM-powered business applications are at the absolute forefront of this transformation — commanding exceptional compensation, immediate hiring priority, and strategic career positioning in one of the world’s most forward-looking technology markets. For AI engineers who want to work on genuinely consequential enterprise AI deployments — not academic research or internal tools — Abu Dhabi in 2026 offers an unparalleled combination of technical challenge, professional impact, career acceleration, and financial reward in a completely tax-free employment environment.
Your Career Growth Path: Senior GenAI Engineer → Principal AI Engineer → AI Architect → Head of AI Engineering → VP of AI → Chief AI Officer — a globally recognized, technically elite, and financially exceptional AI engineering career built on some of the most ambitious enterprise GenAI programs operating anywhere in the world today.
Who Should Apply?
- Senior AI Engineers: With 6–8+ years of AI/software experience and 3–4+ years of production GenAI delivery using Azure OpenAI, LangChain, or equivalent frameworks
- LLM & RAG Architects: With end-to-end RAG pipeline design, vector database configuration, and embedding optimization experience in enterprise production environments
- Agentic AI Developers: With multi-agent workflow design, AutoGen or Semantic Kernel orchestration, and autonomous AI task execution experience in commercial applications
- Azure AI Platform Engineers: With Azure OpenAI, Azure AI Studio, AKS, and CI/CD delivery experience ready to lead GenAI architecture decisions in Abu Dhabi
- GenAI Technical Leads: Who combine deep LLM engineering expertise with team leadership, architecture influence, and strong cross-functional communication skills
- International AI Engineers: From anywhere in the world willing to relocate to Abu Dhabi for a high-impact, well-compensated, tax-free Generative AI technical lead career
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