FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Senior Developer – Generative AI
e360Advanced Generative AI Developer responsible for designing, building, and deploying solutions on Google Cloud. Requires strong Python skills and Google Cloud expertise for client-facing projects.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
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.
Highest-signal resume keywords
Google Cloud Application DevelopmentGenerative AI SolutionsPython DevelopmentAPI Development with FastAPIData Pipeline Engineering
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonGoogle CloudGenerative AIGoogle Agent Development KitVertex AIBigQueryREST APIsSQLDockerCI/CD
Tools & Technologies
Agent EngineCloud RunGKECloud FunctionsMCPSecret ManagerIAMEvent-Driven ArchitectureMicroservicesInfrastructure as Code
Industry Keywords
Data ServicesRetrieval-Augmented GenerationAsynchronous ProcessingSemantic SearchVector Databases
Tech Stack
Tools & technologiesBigQueryCloudDockerGoogle Cloud PlatformMicroservicesPythonSQL
About the role
Key responsibilities & impact- 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
What you’ll need- 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
Benefits
Comp & perks- Internal training
- Subsidized external training
- Reimbursement for technology certification exams and renewals
- Opportunities for mentorship