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Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsGoogle Cloud PlatformKubernetesMicroservicesTerraform
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- 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