AI Solution Architect

About the role

The AI Solution Architect will own the end‑to‑end architecture of AI‑native applications, ensuring seamless integration of AI capabilities, data, and infrastructure. This senior role requires extensive experience in production system design, AI platforms, and enterprise modernization.

Key responsibilities

• Design end‑to‑end architecture for AI‑native applications, covering integration, data flow, security, and cost optimization.
• Define AI APIs and services, establish architecture standards, reference patterns, and best practices.
• Lead architecture reviews, identify technical, integration, scalability, and modernization risks.
• Architect production ML pipelines, model serving, retraining, and high‑throughput systems.
• Define ML engineering standards for testing, versioning, and CI/CD, and lead root‑cause analysis for critical production issues.
• Drive legacy‑to‑AI‑native modernization initiatives and mentor AI engineers and developers.

Required profile

• 8+ years in production system architecture, including 3+ years focused on AI‑native applications.
• 5+ years of production‑scale ML engineering experience.
• Strong stakeholder management and client‑facing communication skills.
• Proven leadership, mentoring, and technical decision‑making abilities.

Required skills

• Azure AI Foundry, AWS Bedrock, Google Vertex AI.
• Azure ML, AWS SageMaker, Vertex AI Prediction.
• APIs, microservices, event‑driven architecture, API gateways, service mesh, secure integrations.
• LangChain, Hugging Face, knowledge graphs, semantic layers (preferred).
• Deep understanding of AI architecture challenges: latency, non‑determinism, scalability, cost optimization.