Senior AI Solution Architect
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Before you apply
LocationRiyadh, Riyadh Province, Saudi Arabia
Typefull-time
SenioritySenior
Experience8–10 yrs
DepartmentIT
Company size201–500 people
PostedOct 13, 2026 · 3w ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
8-10 years of experience in AI, Machine Learning, data science, software engineering, or AI-focused solution architecture
Proven experience designing and delivering enterprise-scale AI/ML solutions
Strong understanding of Machine Learning, Deep Learning, Natural Language Processing (NLP), and predictive analytics
Hands-on experience with Generative AI, LLMs, prompt engineering, and LLM application development
Experience designing RAG pipelines, embeddings, vector databases, and semantic search solutions
Proficiency in Python
Familiarity with AI/ML libraries such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face
Experience with AI frameworks and orchestration tools such as LangChain, LlamaIndex, or equivalent technologies
Strong knowledge of REST APIs, microservices, distributed systems, and enterprise integration patterns
Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud
Understanding of data engineering concepts, data pipelines, data lakes, data warehouses, and data governance
Knowledge of MLOps practices, CI/CD pipelines, model monitoring, and production deployment
Strong understanding of AI security, data protection, responsible AI, and governance frameworks
Excellent architectural design, analytical, problem-solving, communication, and stakeholder management skills
Experience with enterprise AI platforms such as Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or equivalent services
Experience implementing AI agents, tool calling, multi-agent workflows, and enterprise copilots
Familiarity with vector databases such as Pinecone, Weaviate, Milvus, or pgvector
Experience with containerization and orchestration technologies such as Docker and Kubernetes
Knowledge of model evaluation, hallucination mitigation, guardrails, and AI observability
Experience designing hybrid or multi-cloud AI architectures
Relevant certifications in cloud architecture, AI/ML, or Generative AI
Experience leading technical teams and presenting architecture solutions to senior stakeholders
Skills
Python
PyTorch
TensorFlow
Scikit-learn
Hugging Face
LangChain
LlamaIndex
AWS
Microsoft Azure
Google Cloud
Docker
Kubernetes
Pinecone
Weaviate
Milvus
pgvector
About the role
Job Summary
We are seeking an experienced AI Solution Architect with 8–10 years of experience in Artificial Intelligence (AI), Machine Learning (ML), and enterprise solution architecture. The ideal candidate will be responsible for designing, developing, and implementing scalable, secure, and high-performance AI solutions aligned with business objectives.
The candidate should have strong expertise in AI/ML architecture, Generative AI, Large Language Models (LLMs), cloud-based AI platforms, and enterprise system integration. The role requires close collaboration with business stakeholders, data scientists, software engineers, and technology teams to translate business requirements into effective AI-driven solutions.
Responsibilities
Key Responsibilities
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Design end-to-end AI/ML solution architectures aligned with business requirements and enterprise technology standards.
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Define AI strategy, architecture patterns, technology stacks, and implementation roadmaps.
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Architect and implement AI, ML, Generative AI, and LLM-based solutions for enterprise use cases.
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Design Retrieval-Augmented Generation (RAG) architectures, vector database integrations, and knowledge retrieval pipelines.
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Evaluate and select appropriate AI models, frameworks, platforms, and cloud services based on business and technical requirements.
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Develop architecture for scalable AI applications, APIs, microservices, and enterprise integrations.
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Design and implement AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
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Establish best practices for model deployment, monitoring, versioning, performance optimization, and lifecycle management using MLOps principles.
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Define responsible AI practices, including data privacy, security, explainability, bias mitigation, and governance.
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Assess the feasibility, scalability, cost, and business value of proposed AI solutions.
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Collaborate with data scientists, ML engineers, data engineers, developers, and business stakeholders throughout the solution lifecycle.
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Develop architecture diagrams, technical specifications, proof of concepts (POCs), and implementation plans.
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Guide development teams on AI architecture, coding standards, integration patterns, and deployment practices.
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Monitor emerging AI technologies and recommend innovations that improve business outcomes.
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Ensure AI solutions meet enterprise security, compliance, performance, availability, and reliability requirements.
Requirements
Required Skills and Qualifications
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8–10 years of experience in AI, Machine Learning, data science, software engineering, or AI-focused solution architecture.
•
Proven experience designing and delivering enterprise-scale AI/ML solutions.
•
Strong understanding of Machine Learning, Deep Learning, Natural Language Processing (NLP), and predictive analytics.
•
Hands-on experience with Generative AI, LLMs, prompt engineering, and LLM application development.
•
Experience designing RAG pipelines, embeddings, vector databases, and semantic search solutions.
•
Proficiency in Python and familiarity with AI/ML libraries such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
•
Experience with AI frameworks and orchestration tools such as LangChain, LlamaIndex, or equivalent technologies.
•
Strong knowledge of REST APIs, microservices, distributed systems, and enterprise integration patterns.
•
Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud.
•
Understanding of data engineering concepts, data pipelines, data lakes, data warehouses, and data governance.
•
Knowledge of MLOps practices, CI/CD pipelines, model monitoring, and production deployment.
•
Strong understanding of AI security, data protection, responsible AI, and governance frameworks.
•
Excellent architectural design, analytical, problem-solving, communication, and stakeholder management skills.
Nice to have
Preferred Qualifications
•
Experience with enterprise AI platforms such as Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or equivalent services.
•
Experience implementing AI agents, tool calling, multi-agent workflows, and enterprise copilots.
•
Familiarity with vector databases such as Pinecone, Weaviate, Milvus, or pgvector.
•
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
•
Knowledge of model evaluation, hallucination mitigation, guardrails, and AI observability.
•
Experience designing hybrid or multi-cloud AI architectures.
•
Relevant certifications in cloud architecture, AI/ML, or Generative AI.
•
Experience leading technical teams and presenting architecture solutions to senior stakeholders.
Key Competencies
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AI/ML solution architecture and technology strategy.
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Generative AI, LLMs, and RAG implementation.
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Enterprise architecture and system integration.
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Cloud-native AI solution design.
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MLOps and production AI lifecycle management.
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AI security, governance, and responsible AI.
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Technical leadership and stakeholder collaboration.
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Performance, scalability, and cost optimization.