Senior AI Solution Architect

Tawantech; Riyadh; On-site; SAR 450,000 - 650,000; Full time

Job summary

Tawantech is seeking an experienced AI Solution Architect with a focus on designing scalable, secure AI solutions for enterprise use. The role emphasizes AI/ML architecture, Generative AI, LLMs, and cloud-based platforms to meet business objectives.

The candidate will collaborate with data scientists, software engineers, and stakeholders to translate requirements into effective AI-driven solutions, including RAG architectures and MLOps practices.

Qualifications

• Extensive experience designing enterprise AI architectures.
• Proven track record delivering AI/ML solutions at scale.
• Strong knowledge of NLP, ML, and predictive analytics.

Responsibilities

• Design end-to-end AI/ML solution architectures aligned with business needs.
• Define AI strategy, patterns, stacks, and implementation roadmaps.
• Architect AI/LLM-based solutions for enterprise use cases.
• Design RAG architectures, vector DBs, and knowledge pipelines.
• Evaluate models, platforms, and cloud services per requirements.

Skills

AI/ML architecture

Generative AI/LLMs

Python + ML libraries

REST APIs & microservices

Cloud platforms (AWS/Azure/GCP)

MLOps / CI/CD

Data governance & security

Stakeholder communication

Tools

Docker

Kubernetes

LangChain

LlamaIndex

Job description

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.

Key Responsibilities

• Design end-to-end AI/ML solution architectures aligned with business requirements and enterprise technology standards.
• Define AI strategy, architecture patterns, technology stacks, and implementation roadmaps.
• Architect and implement AI, ML, Generative AI, and LLM-based solutions for enterprise use cases.
• Design Retrieval-Augmented Generation (RAG) architectures, vector database integrations, and knowledge retrieval pipelines.
• Evaluate and select appropriate AI models, frameworks, platforms, and cloud services based on business and technical requirements.
• Develop architecture for scalable AI applications, APIs, microservices, and enterprise integrations.
• Design and implement AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Establish best practices for model deployment, monitoring, versioning, performance optimization, and lifecycle management using MLOps principles.
• Define responsible AI practices, including data privacy, security, explainability, bias mitigation, and governance.
• Assess the feasibility, scalability, cost, and business value of proposed AI solutions.
• Collaborate with data scientists, ML engineers, data engineers, developers, and business stakeholders throughout the solution lifecycle.
• Develop architecture diagrams, technical specifications, proof of concepts (POCs), and implementation plans.
• Guide development teams on AI architecture, coding standards, integration patterns, and deployment practices.
• Monitor emerging AI technologies and recommend innovations that improve business outcomes.
• Ensure AI solutions meet enterprise security, compliance, performance, availability, and reliability requirements.

Required Skills and Qualifications

• 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.

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

• AI/ML solution architecture and technology strategy.
• Generative AI, LLMs, and RAG implementation.
• Enterprise architecture and system integration.
• Cloud-native AI solution design.
• MLOps and production AI lifecycle management.
• AI security, governance, and responsible AI.
• Technical leadership and stakeholder collaboration.
• Performance, scalability, and cost optimization.