Technical Architect – AI

Location : Santa Clara, CA

Job Type : DTH/Contract

Job Description:

• 10–12 years in AI/ML, Cloud, platform engineering, or enterprise architecture roles
• 3+ years of hands-on experience with GCP Vertex AI, including real work with components such as Agentspace, Agent Builder, Vector Search (Matching Engine) amd/or Search & Conversation
• 1 year hands-on experience with Agent Development, Agentspace, Agent Builder, Search & Conversation; able to describe at least one actual solution built on Vertex AI
• 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)
• 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
• 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
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical discipline
• 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.
• The GCP Vertex AI + Agentspace architecture
• Standards for agent development patterns, grounding, and memory
• Integration of agents with SAP/Salesforce/ServiceNow
• A2A (Agent-to-Agent) coordination and orchestration design
• Context Engineering patterns for reliable grounding
• Approaches for testing, observability, safety, and control in GenAI systems
• Enterprise governance, LLMOps, AgentOps, and lifecycle management 

Primary Responsibilities:

• Architect Scalable Vertex AI & Agentspace Solutions – Design and deliver AI architectures built on GCP Vertex AI and Agentspace, covering agent workflows, retrieval pipelines, vector search, grounding logic, tool integrations, and multi-agent (A2A) coordination; Ensure the platform is secure, resilient, and built for scale
• Platform Strategy & Technical Direction – Provide guidance on architecture patterns, technology choices, and platform evolution; Help teams understand trade-offs, make decisions that align with long-term business outcomes
• RAG Systems & Context Engineering – Lead the design of retrieval pipelines and context strategies that produce reliable, high-quality responses; Define how data is chunked, embedded, searched, and assembled into grounded context windows for agents
• Agentic AI Frameworks & A2A Patterns – Define patterns for building and coordinating agents across Agentspace, LangGraph, DSPy, or similar frameworks; Establish approaches for delegation, task planning, error recovery, and safe inter-agent communication
• Tooling Integration & MCP-Style Interfaces – Architect how agents call tools and external systems; Define tool schemas, safety constraints, validation rules, and execution boundaries across SAP, Salesforce, ServiceNow, and enterprise APIs
• LLMOps & AgentOps – Set up operational foundations for prompts, models, and agents — including CI/CD pipelines, monitoring dashboards, version control, error tracking, and cost governance; Implement guardrails that reduce hallucinations and prevent unsafe or unintended behavior
• Design Authority & Governance – Lead architecture reviews, define reference architectures, and establish reusable patterns. Ensure every GenAI initiative adheres to security, data governance, and platform standards
• Cross-Functional Collaboration – Work closely with engineering, data, product, and business teams to convert use cases into practical, production-ready architecture. Break down complexity so teams can execute confidently
• Documentation & Standards – Create and maintain playbooks, best practices, design guides, and reference implementations that help distributed teams build consistently
• Monitoring, Testing & Observability – Establish testing frameworks for retrieval quality, agent behavior, grounding accuracy, and safety signals; Guide development of AgentOps dashboards that track performance, tool failures, latency, drift, and system health.

Secondary Responsibilities:

• Platform Research & Innovation – Stay current with advancements across Vertex AI, Agentspace, Model Garden, and broader agentic patterns. Bring forward ideas worth evaluating and scaling
• Proof of Concepts – Lead/sponsor PoCs to validate feasibility, performance business value before full-scale adoption
• Ecosystem Awareness – Maintain familiarity with Azure OpenAI, Copilot Studio, AI Studio, Cognitive Search, and other cloud AI platforms to support multi-cloud strategy
• Business Alignment – Engage with product and business leaders to identify impactful use cases, help shape roadmaps, and clarify expected outcomes
• Mentorship & Skill Building – Support engineers through coaching on RAG tuning, prompt refinement, agent patterns, testing techniques, and responsible AI practices