Product UX Designer ( e-Commerce )

• Job Title: Gemini AI Architect-Governance Control Tower
• End Client: Zensar
• Work Arrangement: Minimum 3 days/week onsite required
• Duration: Long Term
• Location: San Ramon CA
• Rate: $55/hr. (Inabia W-2)
• NO C2C

ROLE OVERVIEW
This is the architectural owner role for the clients Governance Control Tower the single person who owns its design end to end and who platform leadership deals with directly. This person will define architecture standards and the checklist every agent must clear before going live run the review board relationship and lead a cross-functional delivery pod that implements and then operates the platform staying hands-on through both a build phase (standing up the Control Tower) and a run phase (transitioning to a right-sized support pod while remaining the architectural owner). The role spans five workstreams: Governance & Standards Registry & Gateway Operations Connector & Retrieval Engineering Identity & Entitlement Enforcement and Observability & Cost Control.
KEYWORDS
Google Gemini Enterprise Google ADK Agent Registry MCP RAG AI Governance Identity & Entitlements Observability & FinOps Python
KEY RESPONSIBILITIES
Design the reusable top-level agent layer (orchestrator context engineering retrieval synthesis response) as the common entry point for every application on the platform
Lead the delivery pod: set technical direction review work and be accountable for what ships
Translate between platform leadership and the delivery team turning direction into architecture and architecture into a defensible plan
Hold the quality bar evaluation datasets threshold gates and regression testing so the platform stays reliable as agent count scales
Own architecture across Governance & Standards Registry & Gateway Operations Connector & Retrieval Engineering Identity & Entitlement Enforcement and Observability & Cost Control

REQUIREMENTS / MUST-HAVES
10 years in software engineering and architecture with 3 years designing and running applied AI systems in production
Proven experience as the architectural owner of an enterprise platform set standards other teams had to follow and made them stick
Hands-on with Google Gemini Enterprise and ADK or a directly comparable enterprise agent platform including runtime registration identity and observability
Deep experience with multi-agent systems in production orchestration routing tool use memory human-in-the-loop with real operational ownership
Strong grounding in RAG and retrieval architecture: vector stores embedding models chunking strategy hybrid search
Identity and access depth: OAuth2 SAML RBAC token exchange service-account vs. end-user credential propagation document-level ACL mapping into a retrieval layer
Proficient in Python; comfortable with Go or an equivalent second language
Experience with MCP building servers not only consuming them
Solid cloud-native and systems fundamentals GCP strongly preferred (Cloud Run GKE Vertex AI networking IAM)
Cost awareness at scale token and inference spend management across a growing agent estate