Generative AI Architect

Role & responsibilities

• Lead the architecture, design, and implementation of enterprise Generative AI solutions across business functions.
• Design scalable LLM, RAG, AI Agent, and multi-agent architectures for production environments.
• Translate business requirements into secure, scalable, reliable, and cost-effective GenAI solutions.
• Evaluate and select appropriate LLMs, foundation models, vector databases, AI frameworks, cloud services, and GenAI platforms.
• Develop and govern enterprise GenAI architecture standards, reference architectures, design patterns, and best practices.
• Build and oversee POCs and prototypes, and guide their transition from experimentation to production.
• Design RAG pipelines, including document ingestion, chunking, embeddings, vector search, retrieval, reranking, context management, and response generation.
• Architect AI Agent and multi-agent systems using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent technologies.
• Establish LLMOps/MLOps practices covering model deployment, monitoring, evaluation, observability, versioning, and lifecycle management.
• Ensure GenAI solutions meet enterprise requirements for security, privacy, governance, responsible AI, compliance, and access control.
• Optimize LLM performance, latency, scalability, reliability, and infrastructure costs.
• Collaborate with data engineering, software engineering, cloud, cybersecurity, product, and business teams.
• Provide technical leadership and mentorship to AI/ML engineers, data scientists, and software development teams.
• Assess emerging GenAI models, tools, frameworks, and technologies and recommend their adoption where appropriate.
• Support technical discussions with clients and senior stakeholders, including solution presentations, architecture reviews, technical proposals, and POCs.
• Define technical KPIs and ensure AI solutions deliver measurable business value and production outcomes.

Preferred candidate profile

• 5 to 10 years of experience in AI/ML, software engineering, cloud architecture, solution architecture, data science, or related technology roles.
• Strong hands-on experience designing and delivering Generative AI and LLM-based enterprise solutions.
• Deep understanding of LLMs, RAG, AI Agents, Agentic AI, prompt engineering, embeddings, vector search, fine-tuning, and LLM evaluation.
• Strong programming experience in Python, with experience developing production-grade APIs and AI applications.
• Experience with Azure OpenAI, Azure AI Foundry, AWS Bedrock, Amazon SageMaker, Google Vertex AI, OpenAI, Hugging Face, or equivalent platforms.
• Strong experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar GenAI frameworks.
• Hands-on knowledge of vector databases/search technologies such as Pinecone, FAISS, Weaviate, Milvus, Elasticsearch/OpenSearch, or Azure AI Search.
• Strong understanding of cloud architecture, microservices, REST APIs, Docker, Kubernetes, CI/CD, Terraform, and distributed systems.
• Experience implementing MLOps/LLMOps, including model lifecycle management, evaluation, monitoring, observability, and production optimization.
• Strong knowledge of AI security, data privacy, Responsible AI, AI governance, RBAC, API security, and enterprise compliance.
• Experience with enterprise architecture, solution design, system design, integration patterns, and technical documentation.
• Ability to balance technical feasibility, business requirements, scalability, performance, security, and cost.
• Strong stakeholder management and communication skills, with the ability to explain complex AI concepts to technical and non-technical leadership.
• Experience leading technical teams and mentoring AI/ML engineers and developers.
• Experience in banking, financial services, government, telecom, healthcare, or other regulated industries is an advantage for UAE-based roles.
• Bachelor's degree in Computer Science, IT, AI/ML, Data Science, Software Engineering, or a related technical discipline; Master's degree is preferred.
• Relevant certifications in Azure/AWS/GCP, AI/ML, Cloud Architecture, Kubernetes, TOGAF, or Generative AI are desirable