AI Foundation Architect

• 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
• Balance business needs, technical requirements, and production constraints
• Communicate technical decisions clearly to clients and create reusable architecture standards and documentation

• 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
• 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
• Experience designing multi-agent systems, advanced RAG architectures, or multi-model AI solutions
• Knowledge of MLOps, LLMOps, CI/CD, Kubernetes, Terraform, and infrastructure automation
• Experience with modern data platforms such as Databricks, Snowflake, Microsoft Fabric, or comparable technologies
• Familiarity with vector databases, AI gateways, event-driven architectures, or model-serving platforms
• Experience with enterprise architecture frameworks, architecture reviews, or regulated environments
• Experience in consulting or professional services, including technical workshops and architecture assessments

• International and multicultural projects
• Opportunities to work with leading technologies in Data, Cloud, and AI ecosystems
• Access to training programs and continuous professional development
• Professional growth opportunities and internal mobility
• A culture built on collaboration, innovation, and continuous learning