Senior Developer – Generative AI

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 Big Query, 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 work streams and provide design reviews, code reviews, and guidance to other developers
• Participate in client discovery, architecture, testing, deployment, and knowledge‑transfer activities

• 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 Big Query 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

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 tools to create scalable, secure, and efficient solutions.

• Google Cloud Application Development
• Generative AI Solutions
• Python Development
• API Development with FastAPI
• CI/CD and Automated Testing

• Python
• Google Cloud
• Generative AI
• Google Agent Development Kit
• Vertex AI
• Big Query
• REST APIs
• SQL
• Docker
• Infrastructure as Code

• Problem‑Solving
• Collaboration
• Technical Guidance

• Microservices
• Event‑Driven Architecture
• Data Pipelines
• MCP Integrations
• Asynchronous Processing

• Agent Engine
• Cloud Run
• GKE
• Cloud Functions
• Document AI
• Secret Manager
• IAM