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

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

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

- 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.

- 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.

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

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

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

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

- 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.