Job Description

Principal AI Engineer / AI Solutions Architect – GenAI & Agentic AI Location: Abu Dhabi, UAE Employment Type: Full-time Workplace: On-site About the Role Digital Next is looking for an exceptional, hands-on Principal AI Engineer / AI Solutions Architect – GenAI & Agentic AI to join our technology team in Abu Dhabi and work across major enterprise and government digital transformation programmes. This is a senior technical role for someone who operates at the forefront of AI engineering. We are looking for an exceptional technologist who can move from: Complex business problem → technology research → solution architecture → rapid prototype → production deployment. You will work across Digital Next's strategic programmes while also helping us identify and develop new AI capabilities, reusable accelerators and emerging technology solutions that differentiate Digital Next in the market. This is not a junior AI, prompt-engineering, purely research, or management-only position. You must be able to personally architect and build. We are looking for someone who continuously explores the latest models, agent architectures, frameworks, development tools and engineering techniques, understands where the AI market is moving, and can determine which technologies are genuinely ready for enterprise adoption. You should be equally comfortable architecting complex enterprise AI solutions, writing production-quality code, rapidly building prototypes, evaluating emerging technologies, troubleshooting difficult engineering problems, and defending your technical decisions to senior stakeholders. Key Responsibilities • Act as a senior technical authority for AI initiatives, challenging weak architectural assumptions and recommending appropriate models, frameworks, platforms and engineering patterns. • Own AI solutions technically from discovery and architecture through implementation, evaluation, deployment and production optimization. • Architect and build advanced Generative AI and LLM applications for enterprise and government use cases. • Design production-grade RAG architectures, including content ingestion, chunking, embeddings, hybrid/vector search, semantic retrieval, reranking, grounding and citations. • Design and build AI agents and multi-agent systems that securely interact with enterprise applications, data, APIs and tools. • Implement MCP (Model Context Protocol), tool/function calling and modern agent orchestration patterns. • Design advanced knowledge retrieval, knowledge graph and GraphRAG solutions where appropriate. • Evaluate and integrate frontier commercial and open-source AI models based on capability, quality, security, sovereignty, latency, scalability and cost. • Develop production-grade Python services, APIs, AI microservices and integration components. • Master modern AI-native software engineering, using coding agents and AI development tools to accelerate architecture exploration, implementation, testing, debugging, refactoring and documentation—while maintaining full technical ownership of generated code, security and engineering quality. • Develop multimodal AI solutions involving documents, images, vision, speech and other modalities where appropriate. • Design AI evaluation frameworks covering accuracy, retrieval quality, groundedness, hallucination, safety, latency, performance and cost. • Implement AI security controls addressing prompt injection, indirect prompt injection, data leakage, insecure tool invocation, excessive agent permissions and other AI-specific threats. • Integrate AI capabilities with enterprise platforms including CMS/DXP, DMS, search platforms, databases, APIs and business applications. • Design AI solutions for cloud, private-cloud, sovereign and controlled enterprise environments. • Support deployment, observability, scaling, performance optimization and operationalization of production AI workloads. • Rapidly build PoCs, technical experiments and technology demonstrators, then convert successful concepts into production-grade solutions. • Continuously research and experiment with newly emerging AI models, frameworks, tools and architectural approaches, assessing their practical enterprise applicability. • Identify new AI-enabled capabilities and opportunities that can create measurable value for Digital Next and its clients. • Lead technical discovery sessions, architecture discussions, workshops, PoCs and solution demonstrations with enterprise and government stakeholders. • Contribute to Digital Next's reusable AI architectures, accelerators, engineering standards, reference implementations and technical capabilities. • Provide technical leadership, conduct technical reviews and mentor engineers across AI initiatives. Required Experience & Skills • 8+ years of professional software, data, ML or AI engineering experience. • Significant recent hands-on AI engineering experience. • Proven experience personally architecting and developing production Generative AI / LLM solutions. • Strong production experience designing and implementing RAG architectures. • Expert-level Python and software engineering capabilities. • Strong experience with embeddings, vector databases/search, semantic search, hybrid retrieval and reranking. • Hands-on experience designing AI agents, tool-using systems, function calling and agentic workflows. • Understanding of MCP and modern agent/tool integration architectures. • Experience with Azure AI / Azure OpenAI or comparable enterprise AI platforms. • Experience evaluating and working with both commercial frontier models and open-source LLM ecosystems. • Strong REST API, backend and enterprise integration experience. • Experience with Docker, Kubernetes and modern CI/CD practices. • Strong understanding of enterprise AI security, responsible AI, governance, privacy and data protection. • Experience implementing LLM/RAG evaluation, monitoring and observability. • Understanding of model selection, context engineering, structured outputs, tool calling, model limitations and cost/performance optimization. • Ability to take complex AI solutions from concept → research → architecture → prototype → production. • Strong technical problem-solving and architectural decision-making capability. • Demonstrated ability to independently learn, evaluate and apply emerging technologies. Highly Desirable • Advanced agent orchestration and multi-agent architectures. • Deep knowledge of MCP and agent/tool ecosystems. • Hugging Face and open-source AI/model ecosystems. • Local/private LLM deployment and model serving. • Knowledge graphs and GraphRAG. • Adva

Requirements

Required Experience & Skills

- 8+ years of professional software, data, ML or AI engineering experience.

- Significant recent hands-on AI engineering experience.

- Proven experience personally architecting and developing production Generative AI / LLM solutions.

- Strong production experience designing and implementing RAG architectures.

- Expert-level Python and software engineering capabilities.

- Strong experience with embeddings, vector databases/search, semantic search, hybrid retrieval and reranking.

- Hands-on experience designing AI agents, tool-using systems, function calling and agentic workflows.

- Understanding of MCP and modern agent/tool integration architectures.

- Experience with Azure AI / Azure OpenAI or comparable enterprise AI platforms.

- Experience evaluating and working with both commercial frontier models and open-source LLM ecosystems.

- Strong REST API, backend and enterprise integration experience.

- Experience with Docker, Kubernetes and modern CI/CD practices.

- Strong understanding of enterprise AI security, responsible AI, governance, privacy and data protection.

- Experience implementing LLM/RAG evaluation, monitoring and observability.

- Understanding of model selection, context engineering, structured outputs, tool calling, model limitations and cost/performance optimization.

- Ability to take complex AI solutions from concept → research → architecture → prototype → production.

- Strong technical problem-solving and architectural decision-making capability.

- Demonstrated ability to independently learn, evaluate and apply emerging technologies.

Responsibilities

Key Responsibilities

- Act as a senior technical authority for AI initiatives, challenging weak architectural assumptions and recommending appropriate models, frameworks, platforms and engineering patterns.

- Own AI solutions technically from discovery and architecture through implementation, evaluation, deployment and production optimization.

- Architect and build advanced Generative AI and LLM applications for enterprise and government use cases.

- Design production-grade RAG architectures, including content ingestion, chunking, embeddings, hybrid/vector search, semantic retrieval, reranking, grounding and citations.

- Design and build AI agents and multi-agent systems that securely interact with enterprise applications, data, APIs and tools.

- Implement MCP (Model Context Protocol), tool/function calling and modern agent orchestration patterns.

- Design advanced knowledge retrieval, knowledge graph and GraphRAG solutions where appropriate.

- Evaluate and integrate frontier commercial and open-source AI models based on capability, quality, security, sovereignty, latency, scalability and cost.

- Develop production-grade Python services, APIs, AI microservices and integration components.

- Master modern AI-native software engineering, using coding agents and AI development tools to accelerate architecture exploration, implementation, testing, debugging, refactoring and documentation—while maintaining full technical ownership of generated code, security and engineering quality.

- Develop multimodal AI solutions involving documents, images, vision, speech and other modalities where appropriate.

- Design AI evaluation frameworks covering accuracy, retrieval quality, groundedness, hallucination, safety, latency, performance and cost.

- Implement AI security controls addressing prompt injection, indirect prompt injection, data leakage, insecure tool invocation, excessive agent permissions and other AI-specific threats.

- Integrate AI capabilities with enterprise platforms including CMS/DXP, DMS, search platforms, databases, APIs and business applications.

- Design AI solutions for cloud, private-cloud, sovereign and controlled enterprise environments.

- Support deployment, observability, scaling, performance optimization and operationalization of production AI workloads.

- Rapidly build PoCs, technical experiments and technology demonstrators, then convert successful concepts into production-grade solutions.

- Continuously research and experiment with newly emerging AI models, frameworks, tools and architectural approaches, assessing their practical enterprise applicability.

- Identify new AI-enabled capabilities and opportunities that can create measurable value for Digital Next and its clients.

- Lead technical discovery sessions, architecture discussions, workshops, PoCs and solution demonstrations with enterprise and government stakeholders.

- Contribute to Digital Next's reusable AI architectures, accelerators, engineering standards, reference implementations and technical capabilities.

- Provide technical leadership, conduct technical reviews and mentor engineers across AI initiatives.

Skills Required

PythonEmbeddingsVector databasesSemantic searchHybrid retrievalRAG architecturesAI agentsTool callingMCPAzure AI / Azure OpenAIOpen-source LLM ecosystemsREST APIDockerKubernetesCI/CDAI securityGovernance & privacyData protectionModel evaluation & observabilityContext engineeringQuestion answering / groundingGraphRAGKnowledge graphs

Principal AI Engineer / AI Solutions Architect – GenAI & Agentic AI

Job Description

Requirements

Responsibilities

Skills Required

Interested in this role?

Similar jobs in the UAE