Senior Cloud Platform Engineer
- Role
- AI / ML
- Experience
- Senior
Open to US only. Set where you work from to check your eligibility.
No BS summary
Senior Cloud Platform Engineer needed to design and deploy AI agents using Google Gemini models on GCP Vertex AI. Must have strong GCP and Vertex experience, IaC (Kubernetes, Terraform, CI/CD), and enterprise environment navigation skills. Experience with AWS/Azure, Bedrock/Azure Agent Foundry, or LangGraph/LangChain is a plus.
Core skills
Required skills
Optional skills
The person would be working at the intersection of cloud infrastructure and AI/LLM engineering — specifically: Designing and deploying AI agents using Google’s Gemini models Using GCP’s Vertex AI as the primary ML platform (model hosting, pipelines, endpoints) Building with Google’s agent tooling — the Agent Development Kit (ADK) for constructing agent logic, and the Managed Agents API for running/orchestrating them at scale Likely involved in things like tool-calling, multi-agent orchestration, RAG pipelines, and connecting agents to enterprise systems Role Responsibilities Architecting and implementing infrastructure as code (IaC) . Defining administrative choices for environments that are auto-configured by IaC. Focus on agent use cases , including relationships with BigQuery databases, enterprise connectors, and agent orchestration. Heavy leverage of Vertex . Investigating and tweaking designs to avoid issues with Pfizer’s enterprise infrastructure and GCP limitations. Required Skills and Experience Strong background in GCP and Vertex . Experience with IaC technologies like Kubernetes, Terraform, and CI/CD. Enterprise environment experience is crucial to navigate existing GCP and ISRM teams and strictures. Preferred transferable skills (due to rarity of specific Google ADK experience): Mature experience in AWS or Azure . Experience with Bedrock or Azure Agent Foundry . GCP experience combined with LangGraph or LangChain .
What you'll do
- Designing and deploying AI agents using Google’s Gemini models
- Using GCP’s Vertex AI as the primary ML platform (model hosting, pipelines, endpoints)
- Building with Google’s agent tooling — the Agent Development Kit (ADK) for constructing agent logic, and the Managed Agents API for running/orchestrating them at scale
- Likely involved in things like tool-calling, multi-agent orchestration, RAG pipelines, and connecting agents to enterprise systems
- Architecting and implementing infrastructure as code (IaC)
- Defining administrative choices for environments that are auto-configured by IaC
- Focus on agent use cases , including relationships with BigQuery databases, enterprise connectors, and agent orchestration
- Heavy leverage of Vertex
- Investigating and tweaking designs to avoid issues with Pfizer’s enterprise infrastructure and GCP limitations
What they require
- Strong background in GCP and Vertex
- Enterprise environment experience is crucial to navigate existing GCP and ISRM teams and strictures