Technical Architect (Gen AI)

San R&D Business Solutions LLC•Santa Clara, CA

About The Position

This is a senior architecture role with direct responsibility for how AI capabilities are designed, integrated, and deployed across the enterprise. As a Principal AI Architect specializing in GCP Vertex AI and Agentic AI, you will guide the architecture, strategy, and delivery of enterprise-grade AI platforms. You’ll work closely with engineering, platform, and business teams to shape the AI roadmap, design scalable agentic systems, and ensure responsible adoption of Generative AI across the organization.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical discipline.
14+ years in AI/ML, cloud, platform engineering, or enterprise architecture roles.
3+ years hands-on with GCP Vertex AI, Vector Search
1 Year hands-on with year with Agent Development and hands-on experience with Agentspace, Agent Builder, Search & Conversation
Prior experience designing enterprise-grade AI, GenAI or agentic systems including aspect of Agent Ops and Ob Behalf of Workflows
Exposure to multi-cloud AI environments (Azure OpenAI, Copilot Studio, OpenAI API)
3+ years of hands-on experience with GCP Vertex AI, including real work with components such as Agentspace, Agent Builder, Vector Search (Matching Engine), or Search & Conversation. The candidate must be able to describe at least one actual solution built on Vertex AI.
2+ years of experience building Generative AI applications, such as AI assistants, retrieval-based systems, or LLM-powered workflows. The candidate should clearly explain what they built and what their role was.
5+ years of strong Python development experience, specifically building backend services, APIs, microservices, or automation components used in production environments.
Practical integration experience with at least one enterprise platform (SAP, Salesforce, or ServiceNow), with the ability to describe a real integration scenario they worked on.
3+ years of cloud deployment experience, preferably using GCP services like Cloud Run, Cloud Functions, or Kubernetes for deploying and maintaining cloud-native applications.
1–2 years of experience operationalizing AI systems, including managing prompts or models, handling errors or failures, monitoring performance, or improving system reliability. Exposure to LLMOps or similar processes is sufficient.
Basic working knowledge of enterprise security and data protection, including responsible handling of sensitive data, access control, and safe use of AI systems in an enterprise environment.
Strong communication skills, with the ability to explain past projects clearly, walk through their contributions, and provide understandable examples of their AI and cloud experience.