AI Foundation Architect

Why Keyrus, Why Now

Keyrus is an international group of 3,000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does.

For more than 30 years, we have helped organizations build the data foundations that power intelligent systems. Today, we go further by designing and operationalizing intelligence that delivers measurable business value.

As AI becomes part of everyday business operations, organizations need reliable architectures that connect models, agents, data, applications, cloud infrastructure, security, and human oversight.

At Keyrus, you'll help define the foundations that make intelligent systems scalable, secure, and reliable in production.

Technology amplifies. Keyrus culture differentiates. Industrial discipline connects the two.

What you'll architect

As an AI Foundation Architect, you will design and define the technical foundations that enable AI and agentic systems to operate reliably at enterprise scale.

You will connect AI models, enterprise data, applications, APIs, cloud services, and security controls into complete architectures that engineering teams can implement and maintain.

Your focus will be on architecture decisions, technical standards, system integration, and production readiness. You'll work closely with clients and technical teams to ensure solutions are scalable, secure, cost-effective, and aligned with business needs.

Your responsibilities

• Define scalable architectures for production AI and GenAI solutions, connecting models, agents, data platforms, enterprise applications, APIs, and cloud infrastructure.
• Establish architecture principles, reference designs, integration patterns, and technical standards for engineering teams.
• Make and document key architecture decisions involving cloud platforms, model integration, data access, security, scalability, performance, and cost.
• Review technical designs and guide engineering teams to ensure solutions follow the agreed architecture and production requirements.
• Design secure and reliable AI foundations, including identity management, access controls, monitoring, resilience, and human oversight.
• Collaborate with AI Engineers, Software Engineers, Data Engineers, and Governance specialists to align technical solutions across different areas.
• Work directly with clients and technical stakeholders to understand requirements, explain architecture decisions, manage technical risks, and develop reusable architecture solutions.

Who you are

You are an experienced technical architect with strong knowledge of enterprise systems, cloud platforms, and modern AI technologies.

You understand how different technologies connect and can make practical architecture decisions based on security, performance, scalability, cost, and business requirements.

You combine technical leadership with strong communication skills and enjoy working directly with clients and engineering teams to solve complex challenges.

What you bring

Experience

• 8+ years of experience designing complex technical architectures, subject to local seniority confirmation.
• Strong experience designing and guiding architecture for production enterprise applications and distributed systems.
• Practical experience architecting production GenAI, LLM, or agentic solutions.
• Experience making technical decisions across cloud infrastructure, applications, integrations, security, and operational systems.
• Experience defining architecture standards, reference designs, technical requirements, and integration patterns.
• Strong communication and consulting skills, including experience working with clients, engineering leaders, and technical stakeholders.

Technical skills

• Strong architecture expertise in at least one major cloud ecosystem, such as AWS, Azure, or GCP.
• Experience with enterprise AI services and model integration, such as Amazon Bedrock, Azure AI, Vertex AI, OpenAI APIs, or comparable platforms.
• Strong knowledge of distributed systems, microservices, APIs, and enterprise integration architectures.
• Understanding of cloud security, identity and access management, scalability, resilience, and observability.
• Knowledge of data architectures, including Lakehouse, streaming, data platforms, and enterprise data integration.
• Understanding of production AI architectures, including RAG, AI agents, model access, tool integration, and human oversight.

Nice to have

• Experience designing multi-agent systems, advanced RAG architectures, or multi-model AI solutions.
• Knowledge of MLOps, LLMOps, CI/CD, Kubernetes,