Key skills
AIAgenticAzureGoogle CloudDynatraceLeadershipStakeholder Management
About this role
Role Summary
We are looking for a highly skilled AI Architect to lead the design and implementation of production-grade AI agent architectures with Google Cloud Platform as the primary cloud platform and Azure as a secondary platform. This role will focus on building robust, scalable, secure, and observable AI systems that support enterprise-grade agentic applications.
The ideal candidate will have hands-on experience architecting and delivering AI agents in production, including critical components such as short-term and long-term memory, evaluation services, offline and online evaluation frameworks, AI Gateway, guardrails, tracing, and observability. Experience with Google Cloud Platform-native services and enterprise monitoring platforms such as Dynatrace is essential.
This role also requires strong stakeholder management skills, the ability to work across multiple customer groups, and comfort operating in aggressive delivery timelines.
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Key Responsibilities
Architect and implement production-grade AI agent solutions on Google Cloud Platform (Google Cloud Platform), with Azure as a supporting cloud environment where required.
Design end-to-end AI systems including:
o Agent orchestration
o Short-term memory
o Long-term memory
o Context management
o Tool integrations
o Workflow execution
o Evaluation pipelines
Build and standardize architecture for production-ready AI agents, ensuring scalability, resilience, security, and maintainability.
Define and implement AI Gateway patterns for model access, routing, authentication, rate limiting, and policy enforcement.
Design and deploy guardrails for responsible AI, safety, compliance, prompt protection, hallucination mitigation, and output validation.
Establish tracing and observability frameworks for AI applications using Google Cloud Platform-native monitoring capabilities and Dynatrace.
Implement evaluation services for AI systems, including:
o Offline evaluation
o Online evaluation
o Model and agent performance benchmarking
o Quality and reliability measurement
Define and implement architecture for memory systems, including short-term conversational memory and persistent long-term memory.
Collaborate with engineering, data, product, security, and customer stakeholders to align AI solutions with business and technical requirements.
Lead technical discussions with different customer stakeholders, translating business needs into scalable AI architectures.
Drive architecture decisions under aggressive timelines while maintaining delivery quality and engineering rigor.
Ensure AI systems adhere to enterprise standards for governance, privacy, security, compliance, and operational excellence.
Provide technical leadership, mentorship, and architecture guidance to cross-functional teams.