AI Solution Architect

About the role

The AI Solution Architect owns the architecture of AI-native applications, ensuring seamless integration across AI capabilities, applications, data and infrastructure. The role requires 8+ years of experience in architecting production systems, with strong expertise in AI, cloud platforms, ML engineering and enterprise modernization.

Key responsibilities

• Design end-to-end architecture for AI-native applications, including integration, data flow, security and cost.
• Design AI APIs and services and define how AI capabilities integrate with applications.
• Establish architecture standards, reference patterns and best practices.
• Lead architecture reviews and identify technical, integration, scalability and modernization risks.
• Architect production ML pipelines, model serving, retraining and high-throughput systems.
• Define ML engineering standards covering testing, versioning and CI/CD.
• Lead root-cause analysis for critical production ML issues.
• Drive legacy-to-AI-native modernization initiatives.
• Partner with QA/AI Assurance teams to ensure architectural testability.
• Mentor AI Engineers and Developers on architecture and design standards.
• Present and defend architecture decisions to client technical leadership.

Required profile

• 8+ years of experience in production system architecture, including 3+ years in AI-native application architecture.
• 5+ years of production-scale ML engineering experience.
• Experience in legacy modernization and AI-native transformation.
• Strong architecture documentation and ADR writing skills.
• Excellent stakeholder management and client-facing communication skills.
• Strong leadership, mentoring and technical decision-making abilities.

Required skills

• Strong expertise in Azure AI Foundry, AWS Bedrock and Google Vertex AI.
• Experience with Azure ML, AWS SageMaker and Vertex AI Prediction.
• Knowledge of APIs, microservices, event-driven architecture, API gateways, service mesh and secure integrations.
• Preferred: LangChain, Hugging Face, knowledge graphs and semantic layers.
• Deep understanding of AI architecture challenges such as latency, non-determinism, scalability and cost optimization.