Senior Generative AI Developer – Google Cloud

Jobtailor

Irvine (CA)

On-site

USD 140,000 - 210,000

Full time

14 days+

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Job summary

Jobtailor is seeking a Senior AI/Cloud Engineer to design, build, test, and deploy Generative AI applications on Google Cloud. You will work with Gemini models, Vertex AI, and Google Agent Development Kit to create agentic solutions integrated with enterprise systems.

Responsibilities include API development, data pipelines, secure cloud architectures, and scalable deployments using Cloud Run, GKE, and IAM. A hands-on, results-driven approach is required.

Qualifications

Significant experience developing and deploying applications on Google Cloud.
Advanced Python development experience.
Hands-on experience building Generative AI or agentic applications.
Experience with Google Agent Development Kit, including agents, tools, workflows, sessions, state, and multi-agent patterns.
Experience integrating Gemini models using Vertex AI or Google Gen AI SDKs.
Experience with Agent Engine, Cloud Run, GKE, Cloud Functions, or similar GCP runtimes.
Experience designing and implementing RAG solutions.
Experience with BigQuery and Google Cloud data services.
Experience building APIs using frameworks such as FastAPI.
Experience with REST APIs, asynchronous processing, event-driven architecture, and microservices.
Understanding of MCP and its use in connecting agents to enterprise tools and systems.
Experience with SQL, document stores, object storage, embeddings, semantic search, or vector databases.
Experience with Git, automated testing, CI/CD, Docker, and infrastructure as code.
Understanding of Google Cloud IAM, service accounts, Secret Manager, networking, logging, and monitoring.
Ability to evaluate tradeoffs involving model quality, latency, security, scalability, reliability, and cost.

Responsibilities

Design, build, test, and deploy Generative AI applications and intelligent agents on Google Cloud.
Develop single-agent and multi-agent solutions using Google Agent Development Kit.
Integrate Gemini models with enterprise APIs, databases, applications, and business workflows.
Deploy AI applications using Agent Engine, Cloud Run, GKE, or other appropriate GCP services.
Build Retrieval-Augmented Generation solutions using services such as BigQuery, Vertex AI Vector Search, Cloud Storage, and Document AI.
Develop APIs, microservices, agent tools, MCP integrations, and event-driven workflows.
Build data pipelines to ingest, transform, chunk, embed, index, and retrieve structured and unstructured data.
Implement session management, memory, tool calling, human approval, and agent orchestration patterns.
Apply automated testing, CI/CD, logging, monitoring, tracing, evaluation, and cost-management practices.
Implement Google Cloud security using IAM, service accounts, Workload Identity Federation, Secret Manager, and private networking.
Troubleshoot issues across agents, models, APIs, data pipelines, integrations, security, and cloud deployments.
Create architecture diagrams, technical designs, API specifications, deployment guides, and operational documentation.
Own technical workstreams and provide design reviews, code reviews, and guidance to other developers.
Participate in client discovery, architecture, testing, deployment, and knowledge-transfer activities.

Skills

Advanced Python Development

Google Cloud Application Development

Generative AI Application Building

API Development

Data Pipeline Construction

Tools

Google Agent Development Kit

Agent Engine

Cloud Run

GKE

Vertex AI

Cloud Functions

Document AI

Secret Manager

Git

Infrastructure as Code

Job description

Design, build, test, and deploy Generative AI applications and intelligent agents on Google Cloud.
Develop single-agent and multi-agent solutions using Google Agent Development Kit.
Integrate Gemini models with enterprise APIs, databases, applications, and business workflows.
Deploy AI applications using Agent Engine, Cloud Run, GKE, or other appropriate GCP services.
Build Retrieval-Augmented Generation solutions using services such as BigQuery, Vertex AI Vector Search, Cloud Storage, and Document AI.
Develop APIs, microservices, agent tools, MCP integrations, and event-driven workflows.
Build data pipelines to ingest, transform, chunk, embed, index, and retrieve structured and unstructured data.
Implement session management, memory, tool calling, human approval, and agent orchestration patterns.
Apply automated testing, CI/CD, logging, monitoring, tracing, evaluation, and cost-management practices.
Implement Google Cloud security using IAM, service accounts, Workload Identity Federation, Secret Manager, and private networking.
Troubleshoot issues across agents, models, APIs, data pipelines, integrations, security, and cloud deployments.
Create architecture diagrams, technical designs, API specifications, deployment guides, and operational documentation.
Own technical workstreams and provide design reviews, code reviews, and guidance to other developers.
Participate in client discovery, architecture, testing, deployment, and knowledge-transfer activities.

Requirements

Significant experience developing and deploying applications on Google Cloud.
Advanced Python development experience.
Hands-on experience building Generative AI or agentic applications.
Experience with Google Agent Development Kit, including agents, tools, workflows, sessions, state, and multi-agent patterns.
Experience integrating Gemini models using Vertex AI or Google Gen AI SDKs.
Experience with Agent Engine, Cloud Run, GKE, Cloud Functions, or similar GCP runtimes.
Experience designing and implementing RAG solutions.
Experience with BigQuery and Google Cloud data services.
Experience building APIs using frameworks such as FastAPI.
Experience with REST APIs, asynchronous processing, event-driven architecture, and microservices.
Understanding of MCP and its use in connecting agents to enterprise tools and systems.
Experience with SQL, document stores, object storage, embeddings, semantic search, or vector databases.
Experience with Git, automated testing, CI/CD, Docker, and infrastructure as code.
Understanding of Google Cloud IAM, service accounts, Secret Manager, networking, logging, and monitoring.
Ability to evaluate tradeoffs involving model quality, latency, security, scalability, reliability, and cost.

Core Competencies

Demonstrates expertise in developing and deploying Generative AI applications on Google Cloud, utilizing advanced Python programming and integrating various Google Cloud services. Proficient in building APIs, data pipelines, and implementing security measures while ensuring high-quality, scalable, and reliable solutions.

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