Guide Jobs in Sunnyvale, CA

Role: Enterprise AI Architect

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

12–15 years of experience in AI/ML-related roles, with strong experience in LLMs & Agentic AI (architecture, governance, platform strategy).

3–5 years: Experience designing enterprise AI solutions including Gemini/Vertex AI, agent applications, RAG pipelines, grounding/data connectors, LLM orchestration, and model governance.
5–7 years: Strong expertise designing ingestion frameworks, APIs, data normalization, semantic search, attribute‑level & record‑level access control, PII masking, and connector factory patterns.
Hands‑on experience with Vertex AI, Gemini Enterprise (Agentspace), Vector Search/Matching Engine, Functions, Cloud Run, IAM, Secret Manager, Kubernetes.
2–3 years: Experience defining multi‑agent coordination flows, intent routing, graceful handoff, context propagation, LangChain/LangGraph/DSPy patterns.
Strong knowledge of embedding pipelines, grounding strategies, retrieval optimization, hybrid search, schema‑aware context construction.
2–3 years: Hands‑on experience implementing LLM governance, safety patterns, content moderation, policy enforcement, auditability, model drift tracking.

Job Description (Role / R&R)
As an Enterprise AI Architect specializing in LLMs and Agentic AI, you will drive the architecture, strategy, and enterprise‑scale enablement of AI platforms. You will guide cross‑functional teams and stakeholders to define the AI roadmap, design enterprise AI foundations, and ensure responsible deployment of Gemini/Vertex AI across the organization.

Key Responsibilities
Architect scalable Gemini/Vertex AI solutions and platform capabilities including multi‑agent systems, retrieval design, grounding, and connector ecosystems.
Lead technical governance for AI systems: intake prioritization, risk assessment, identity propagation, data protection, and platform standards.
Partner with business, security, and engineering leaders to define the enterprise AI adoption model and long‑term operating structure.
Evaluate and guide integration of agents built across Copilot Studio, Foundry, Emma, and other platforms into Gemini Agentspace.
Establish patterns for authentication, OBO flows, schema mapping, error taxonomies, and standardized connector design.
Provide design authority across ingestion connectors, action connectors, RBAC/RLS/FLS alignment, and privacy‑by‑design policies.
Lead workshops, reviews, and technical enablement to raise AI maturity across teams.
Define future‑state AgentOps operating model, registry, monitoring, and platform telemetry standards.
Guide performance engineering: latency optimization (<3s p95), reliability targets (≥99.5%), and scale‑ready architecture.
Ensure compliance with organizational data governance, responsible AI, and regulatory expectations.

Communication Skills:
Communicate effectively with internal and customer stakeholders
Communication approach: verbal, emails and instant messages
Interpersonal Skills:
Strong interpersonal skills to build and maintain productive relationships with team members & customer representatives
Provide constructive feedback during code reviews and be open to receiving feedback on your own code.
Problem-Solving and Analytical Thinking:
Capability to troubleshoot and resolve issues efficiently.
Analytical mindset
Ability to bring idea into reality throught technology implementation & adoption
Task/ Work Updates
Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps
Provides regular updates, proactive and due diligent to carry out responsibilities
Soft Skills / Leadership Traits
Executive presence with the ability to influence C‑suite and senior stakeholders.
Excellent communication—can simplify complex AI concepts for non‑technical leaders.
Ability to drive cross‑functional alignment across data, engineering, product, and security teams.
Strong decision‑making and architectural clarity.
High ownership, adaptability, and a mindset geared toward enterprise transformation.

Enterprise‑grade AI architecture, governance frameworks, and operating model defined and operationalized.
AI platform adoption accelerated with measurable value and standardization.
100% compliance with security, privacy, and responsible AI guidelines.
Scalable agentic framework consistently applied across use cases.

• Knowledge of MCP’s and A2A SDK
• Version Control: Proficiency with version control tools like Git.
• Agile Methodologies: Experience working in Agile development environments.