Principal Forward Deployed Architect, Gemini Enterprise Platform (GCP)

Role Summary 

You are the person a client trusts to turn an ambitious Gemini Enterprise vision into a business outcome that lasts. As Principal Forward Deployed Architect on an account, you own the result -- the value the client set out to create — and with it the technical whole that produces that value: the GenAI platform foundation on Google Cloud, the agent landscape built on the Gemini Enterprise Agent Platform (GEAP), the context-graph and data foundation those agents reason over, and the enterprise rollout into the Gemini Enterprise app. 

Where specialist engineers each own an individual agent, MCP server or data pipeline, you own the whole — deep in the agent platform and the context / data foundation, fluent enough across governance, runtime and adoption to design, sequence and defend the program end to end. You are the senior technical counterpart the client's executives call before they have decided what to build; more importantly, you are the reason they keep calling. You make Google Cloud's AI foundation deliver the outcomes. 

This role exists because standing up a production agent ecosystem on GEAP is not a single-layer problem — model choice, agents, grounding graph, governance perimeter and change management are load-bearing on one another — and because our largest clients will accept only one senior technical owner rather than several. 

Deployment Model 

Placed at one large Gemini Enterprise account, or holding technical ownership across two or three smaller concurrent engagements. You may direct AuxoAI delivery teams, including offshore and onshore Forward Deployed Engineers and client engineers, on the same program — you own the design coherence across it. Significant pre-sales involvement is expected: the GEAP target architecture, the GCP landing-zone approach, effort estimates, and the technical case in proposals for the practice's largest Gemini opportunities. 

Key Responsibilities 

Whole-program architecture 

• Own the target architecture across four layers — GCP GenAI platform foundation, the GEAP agent landscape, the context-graph / data foundation, and enterprise adoption — and sequence delivery across all four. 
  

• Set the reference patterns for how agents are built (ground-up in ADK vs. forked and hardened from Agent Garden templates), where they run (Agent Engine managed vs. Cloud Run vs. self-managed GKE), how they are isolated (sandbox strategy), and how they are governed. 
  

• Design the context-graph foundation — BigQuery, BigQuery graph (GQL) and/or Spanner Graph — and the grounding / RAG strategy that connects it to agents, including entity resolution, semantic modelling and retrieval over Vertex AI Vector Search. 
  

• Identify decisions in one layer that are load-bearing for others (e.g., a grounding-data residency choice that constrains the runtime target and the governance perimeter) and force them to resolution before delivery commits, not during it. 
  

• Arbitrate cross-track trade-offs where multiple Forward Deployed Engineers are deployed to the same client, with a written rationale. 
  

• Maintain technical proximity: review agent designs and evaluation results, interrogate trajectory and latency behaviour, participate in incident reviews, and perform selective hands-on work where it materially changes the outcome. 
  

• Represent AuxoAI in the client's security, compliance and architecture review boards, including the model-governance and data-governance forums. 
  

Client and commercial 

• Advise client executives on trade-offs, sequencing, delivery risk and what not to build — including which use cases are not yet safe to automate. 
  

• Own the technical scope, estimate and defence of proposals and statements of work for the account and for major Gemini Enterprise prospects. 
  

• Give AuxoAI leadership an accurate read on delivery risk, including remediation plans and consumption-cost exposure (runtime vCPU-hours, Sessions and Memory events, model tokens, sandbox compute). 
  

Enablement and practice contribution 

• Enable the client's own platform, data and security leadership to operate and extend the agent landscape and context graph, with named client owners for each major component. 
  

• Develop the Forward Deployed Engineers working alongside you on the account, whether or not they report to you. 
  

• Contribute GEAP reference architectures, context-graph patterns, estimation models and governance blueprints that raise the practice standard. 
  

Outcome Ownership 

You are accountable for the outcome, not the artifact. Long after AuxoAI rolls off, the client's agent ecosystem has to keep earning its place — grounded, governed, evaluated and adopted, still delivering the business result it was built for. When an agent delivered under your architecture regresses, leaks data, breaches a policy or loses the users it was meant to serve, you own the explanation to the client and the plan to make it right. 

Technical Environment 

Expert depth in at least two of the areas below; working competence in all. 

Area 

Technologies 

Gemini agent platform (GEAP) 

ADK (agent types, orchestration, tools), Agent Garden (ground-up and template-based builds), Model Garden, Agent Studio, Agents CLI, Agent Engine runtime (managed / Cloud Run / GKE), Sessions & Memory Bank, MCP and A2A 

Context graph & semantics 

BigQuery, BigQuery graph (GQL), Spanner Graph, knowledge-graph and entity-resolution design, semantic layers, Vertex AI Vector Search, RAG / grounding architecture 

Data platform & governance 

BigQuery, Dataform, Dataproc (Spark), Pub/Sub, Dataplex Universal Catalog / Knowledge Catalog (lineage, classification, data quality), Sensitive Data Protection (DLP) 

Platform & runtime 

GCP, Vertex AI / Agent Platform, GKE, Cloud Run, Terraform, Cloud Build / Cloud Deploy, Developer Connect, Artifact Registry, Cloud Trace / OpenTelemetry, IAM, VPC Service Controls 

Agent governance & security 

Agent Gateway, Model Armor, Semantic Governance (Natural Language Constraints), Agent Identity & Registry, Content Protection, Security Command Center 

Enterprise adoption 

Gemini Enterprise app, agent catalog / Agent Gallery publishing, Google Workspace integration, change management and adoption 

Minimum Qualifications 

1. Master's degree in Computer Science, Engineering, Information Systems or a related field, or equivalent practical experience. 
   

1. 12+ years in engineering, architecture or technical delivery leadership, including senior technical ownership of production systems. 
   

1. Expert depth in at least two of: the agent / GenAI platform layer, the context-graph and semantic-modelling layer, the cloud data-platform layer, and the cloud runtime / governance layer — with working competence across the rest, demonstrable through an architecture walkthrough. 
   

1. Delivered at least one production GenAI or agent system on GCP (Vertex AI / Agent Platform) or a directly comparable cloud, including grounding over an enterprise data or knowledge foundation. 
   

1. End-to-end ownership of technical design for at least two client engagements or major cross-team programs, from discovery through production. 
   

1. Experience as the single senior technical counterpart to a client's executive team on an engagement of material size. 
   

1. Hands-on depth in BigQuery and at least one graph or semantic store (Spanner Graph, BigQuery graph, Neo4j or equivalent). 
   

1. Delivery inside at least one regulated environment, with the ability to describe a design decision the regulation forced. 
   

1. Experience owning the technical scope, estimate and defence of a proposal or statement of work. 
   

Preferred Qualifications 

• Hands-on with the Gemini Enterprise Agent Platform specifically — ADK, Agent Garden, Model Garden, Agent Engine — or a rapid, demonstrable path to it from adjacent agent frameworks (LangGraph, CrewAI, Amazon Bedrock Agents, Azure AI Foundry). 
  

• Experience designing and operating MCP servers (off-the-shelf, third-party and custom) and multi-agent (A2A) topologies. 
  

• Experience building a knowledge / context graph for retrieval grounding at enterprise scale. 
  

• Google Cloud Professional certification (Cloud Architect, Machine Learning Engineer, or Data Engineer). 
  

• Consulting, systems-integrator or professional-services background at principal or equivalent level. 
  

• A record of developing senior engineers or architects, and of leading hybrid onshore / offshore teams at scale. 
  

• Experience deciding against a technically attractive approach for commercial, cost or governance reasons, and defending that to both client and internal stakeholders.