Remote Google Ai Jobs in Riverside, CA (NOW HIRING)

Description

Rate: $80 - $90/ hour (Depending on Experience) 

Note: MUST be US Citizen or Green Card Holder 

***NO RECRUITING AGENCIES***

***NO C2C***

***NO Sponsorship available***

About e360's App Engineering

e360 is a 30+ year privately-owned company with a focus on our people, our clients and leading technologies. e360's Cloud Services Division is a rapidly growing business helping clients manage their Cloud technology. Our team is comprised of leaders that focus on delivering innovative consulting solutions that leverage leading and emerging technologies.  

We are a dynamic and entrepreneurial consulting company that offers ample opportunities for professional development and growth suited to each individual's personal and professional goals. We offer internal, and subsidize external, trainings, and reimburse the cost of technology certification exams and / or renewals. Our family-founded business sees work life fit as a core value that all of our practitioners practice - the value you add to your team is more important than the time that you 'clock in and out.' You will have numerous opportunities to interface with senior leadership, and benefit from mentorship internally or through introductions through external networks to support your growth.  

Description

The Advanced Generative AI Developer is a hands-on consultant responsible for designing, building, and deploying production-ready Generative AI and agentic solutions on Google Cloud.

This role requires strong Python and cloud development experience, practical knowledge of Google Agent Development Kit, Gemini, Vertex AI, and GCP-native application and data services. The consultant will work directly with client and project teams to translate business requirements into secure, scalable, and maintainable AI solutions.

What You'll Do

• 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.

Candidates are not expected to have experience with every listed GCP service. However, they must have hands-on experience delivering Generative AI solutions and be able to explain their architecture and implementation decisions.

Preferred Qualifications

• Experience delivering client-facing Google Cloud consulting projects.
• Experience leading a technical workstream from discovery through production deployment.
• Experience deploying ADK agents using Agent Engine, Cloud Run, or GKE.
• Experience implementing MCP servers, custom agent tools, or enterprise integrations.
• Experience with Vertex AI Vector Search, BigQuery Vector Search, Document AI, Apigee, Pub/Sub, Eventarc, or Workflows.
• Experience with Terraform, Cloud Build, Artifact Registry, and automated GCP deployment pipelines.
• Experience implementing AI evaluation, agent testing, observability, guardrails, and cost monitoring.
• Relevant Google Cloud certifications.

Professional Skills

• Strong consulting, communication, and problem-solving skills.
• Ability to translate business requirements into practical technical solutions.
• Ability to explain complex AI and cloud concepts to technical and non-technical stakeholders.
• Strong documentation and technical leadership skills.
• Ability to work independently and manage changing project priorities.
• Ability to identify and communicate technical risks, dependencies, and blockers.
• Willingness to mentor other developers and contribute to reusable delivery standards.

Critical Success Factors

• Ability to independently design and deliver production-ready AI solutions on Google Cloud.
• Strong practical knowledge of Google ADK, Gemini, Vertex AI, and GCP architecture.
• Ability to build agents that securely interact with APIs, data, tools, and enterprise systems.
• Ability to determine when to use agentic, deterministic, serverless, containerized, or managed-service patterns.
• Commitment to security, testing, observability, governance, maintainability, and cost control.
• Ability to own technical workstreams and consistently deliver high-quality client outcomes.