Senior Developer – Generative AI

Jobtailor; Phoenix (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor is seeking a seasoned specialist to design and deploy Generative AI applications and intelligent agents on Google Cloud in Phoenix. You will craft single- and multi-agent solutions with the Google Agent Development Kit, integrating Gemini models with enterprise APIs and data stores to support business workflows.

You will deploy on Cloud Run, GKE, or related services, architect RAG pipelines, and build scalable APIs with FastAPI.

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.
• 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

Python

Generative AI

Google Cloud

Agent development

Vertex AI

APIs

FastAPI

CI/CD

BigQuery

GKE/Cloud Run

Security

Data pipelines

Embeddings/Vector DB

Tools

Google Agent Development Kit

Gemini models integration

Cloud Run

Vertex AI SDKs

Docker

Kubernetes

Job description

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

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
• 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, with a strong focus on integrating advanced AI models and building robust data pipelines. Proficient in utilizing Google Cloud services and implementing best practices for security, testing, and deployment.