Hire senior Google Vertex AI engineers for Gemini and agents
Senior engineers who build on Google Cloud's AI platform, formerly Vertex AI: Gemini models, ADK agents, grounding, and data in BigQuery.
By the Ryz Labs team · Updated October 2026
Hiring Vertex AI engineers through Ryz gets you senior Latin American engineers who build generative AI and machine learning systems on Google Cloud, from Gemini applications to agents deployed on Google's managed runtime. They are the top 1% of the engineers we interview, and they work on your team and in your repos within an hour of US time zones.
What our Vertex AI engineers work on
In April 2026 Google renamed Vertex AI to the Gemini Enterprise Agent Platform. Vertex AI Studio became Agent Studio, and Agent Engine became Agent Runtime. Existing Vertex workloads kept running, and most teams still search for and talk about Vertex AI. Our engineers work across the old and new names and the services underneath. Typical projects:
- Agents built with the open-source Agent Development Kit (ADK) in Python, Java, Go or TypeScript, deployed to Agent Runtime with managed sessions and long-term memory.
- Retrieval over company documents using the platform's RAG Engine, Vertex AI Search or a custom pipeline on BigQuery vector search or AlloyDB.
- Gemini applications that use long context, multimodal input such as PDFs, images and video, and grounding with Google Search or your own data.
- Batch prediction over BigQuery tables for classification, tagging and summarization at warehouse scale.
- Classic ML pipelines on Vertex AI Pipelines with the Model Registry, endpoints and model monitoring.
- Using Claude and open models from Model Garden alongside Gemini when a workload calls for them.
Skills we vet for
- Gemini API on Google Cloud. The Google Gen AI SDK, function calling, controlled generation with response schemas, context caching and safety settings.
- Agent Development Kit. Multi-agent composition, tools, callbacks, evaluation with ADK's eval tooling, and deployment to Agent Runtime or Cloud Run.
- Grounding and retrieval. RAG Engine, Vertex AI Search, BigQuery vector search and AlloyDB, and how to measure retrieval quality.
- Agent interoperability. The A2A protocol for agent-to-agent communication and MCP for tool access.
- Data on Google Cloud. BigQuery, Dataflow and Cloud Storage, and keeping data in place rather than copying it into new systems.
- Security. IAM, service accounts, VPC Service Controls, CMEK and data residency settings by region.
- MLOps. Pipelines, Model Registry, Feature Store and monitoring for skew and drift.
- Cost control. Model tier choice, context caching, batch prediction and provisioned throughput.
How we vet Vertex AI engineers
Recruiters source engineers who have shipped AI on Google Cloud, then our in-house ARC system ranks the pipeline. Candidates complete structured NTRVSTA AI interviews covering Gemini application design, agents and Google Cloud security. Recruiters review candidates before and after the interview and curate the shortlist. AI scores are advisory; humans make the decisions.
Sample interview topics
- You need an agent that answers questions over 10 years of support tickets in BigQuery. Do you build retrieval in BigQuery, use RAG Engine or something else, and why?
- Walk through deploying an ADK agent to Agent Runtime with per-user sessions and memory. What do you store, and for how long?
- A Gemini response schema works in testing but production outputs are sometimes truncated. What do you check?
- Your security team requires VPC Service Controls around all AI services. What breaks, and how do you plan for it?
- When does long context beat retrieval for a document set, and how would you prove it with data?
Ways to hire Vertex AI engineers
| Option | Best for | Trade-offs |
|---|
| Freelance marketplace | A Gemini prototype or notebook experiment | Production experience on Google Cloud AI is thinner than on other clouds, so vetting takes longer. |
| Staffing or recruiting agency | Sourcing Google Cloud profiles | Few screens test ADK, grounding or agent evaluation. |
| In-house recruiting | A permanent AI team on Google Cloud | A narrower candidate pool and long cycles. |
| Ryz Labs staff augmentation | Adding Google Cloud AI skills to a data or product team | You set direction and review the code. Best when your GCP projects and data are already in place. |
| Ryz Labs AI pod team | Building an agent or ML system end to end in your Google Cloud projects | A dedicated pod with a tech lead, ML and backend engineers and weekly demos. Scoped up front. |
Ryz is the wrong choice if you want a packaged AI platform rather than engineers, or hourly gig work without a conversation. For European or Asian time-zone coverage, a global network is a better fit.
Why hire Vertex AI engineers from Latin America
Many Google Cloud AI projects start with data teams who live in BigQuery and want answers fast. Engineers who share your hours can sit with analysts, try a prompt against real tables and show results before the end of the day, rather than waiting on overnight handoffs.
Senior engineers in Latin America often bring data engineering and backend experience from years of work with US companies, which is what a Google Cloud AI project needs underneath the model. They work in English and are comfortable explaining model behavior to data owners and executives.
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FAQ
Is Vertex AI gone?
The name changed, not the workloads. Google renamed Vertex AI to the Gemini Enterprise Agent Platform in April 2026, and existing projects, SDKs and APIs carried over. Our engineers know both the old and the new names.
Can you use non-Google models on the platform?
Yes. Model Garden includes Claude and open models alongside Gemini. Our engineers choose per workload based on your evals, cost and data requirements.
How is it priced?
We provide a custom quote, scoped per team. You get a plan, a price and the names of the people who would do the work before signing.
What are their hours?
They work within an hour of US time zones. Ryz engineers work on your team, reporting to your leads. Talk to us to scope your team.
Questions we didn't answer? Email info@ryzlabs.com.