Opens LinkedIn
- Employment type
- Contract
- Experience level
- Senior · 5+ years
- Posting language
- English
- Working hours
- 40 hours per week
- Seniority
- Entry level
- Application method
- Direct apply is available
Job summary
Design and deploy autonomous AI agents, multi-agent orchestration systems, and scalable GenAI platform components on GCP, integrating enterprise systems and knowledge sources. Build backend APIs and RAG pipelines, and implement workflow automation, monitoring, security, governance, and responsible AI practices.
Job details
Ideal Candidate We are looking for a hands-on engineer who can build and deploy production-grade Agentic AI and multi-agent systems using Google ADK and MCP, while developing scalable AI platform capabilities on GCP. Location: Downtown Toronto, ON, Canada Job Summary We are looking for an experienced AI Platform Developer with strong hands-on expertise in Google Cloud Platform (GCP), Generative AI, and Agentic AI application development. The ideal candidate will have proven experience building autonomous agentic and multi-agent orchestration systems using Google Agent Development Kit (ADK), Model Context Protocol (MCP) tools, and AI platform engineering practices. The role involves designing scalable AI platforms, integrating AI agents with enterprise systems, and developing secure, production-ready GenAI applications. Key Responsibilities Design and develop autonomous AI agents and multi-agent orchestration systems using Google ADK. Build and integrate MCP tools to connect AI agents with enterprise applications, APIs, databases, and internal services. Architect scalable AI platforms and reusable components for enterprise AI applications. Develop and deploy GenAI solutions using Google Cloud Platform and Vertex AI. Build Retrieval-Augmented Generation (RAG) pipelines and integrate enterprise knowledge sources using Vertex AI Search and vector databases. Develop backend services and REST APIs using Python and FastAPI. Integrate AI applications with GCP services, including Cloud Run, BigQuery, and Cloud SQL. Implement workflow orchestration, automation, monitoring, and observability for AI applications. Establish AI governance, security controls, access management, and responsible AI practices. Collaborate with architects, data engineers, AI/ML engineers, and business stakeholders to deliver enterprise-grade solutions. Required Skills Strong hands-on experience with Google Cloud Platform (GCP) and Vertex AI. Practical experience building Agentic AI applications and autonomous AI agents. Experience with Google ADK (Agent Development Kit). Hands-on experience with MCP (Model Context Protocol) tools and integrations. Experience implementing multi-agent architectures and orchestration workflows. Strong Python programming skills and experience with FastAPI or equivalent frameworks. Experience developing Generative AI applications using LLMs. Hands-on experience with RAG, embeddings, semantic search, and vector databases. Experience building REST APIs and integrating enterprise systems. Knowledge of cloud-native application development, platform engineering, and production deployment practices. Preferred Skills Experience with Vertex AI Search, Cloud Run, BigQuery, and Cloud SQL. Familiarity with Kubeflow Pipelines and AI/ML workflow orchestration. Experience with Docker, Kubernetes, CI/CD, and infrastructure automation. Knowledge of AI governance, observability, security, and compliance. Google Cloud Professional Machine Learning Engineer certification is an advantage. Experience in insurance, financial services, or other enterprise environments is preferred.
What you’ll do
Design and deploy autonomous AI agents, multi-agent orchestration systems, and scalable GenAI platform components on GCP, integrating enterprise systems and knowledge sources. Build backend APIs and RAG pipelines, and implement workflow automation, monitoring, security, governance, and responsible AI practices.
Requirements
Requires hands-on experience with GCP and Vertex AI, Google ADK, MCP integrations, agentic and multi-agent systems, and production GenAI applications. Strong Python and API development skills, plus experience with RAG, embeddings, semantic search, vector databases, and cloud-native deployment, are expected.
Listed skills
- Python · Preferred
- REST APIs · Preferred
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Google Cloud Platform
- Vertex AI
- Google ADK
- Model Context Protocol
- Agentic AI
- Multi-Agent Orchestration
- Python
- FastAPI
- Generative AI
- Retrieval-Augmented Generation
- Vector Databases
- REST APIs
- Vertex AI Search
- Cloud Run
- BigQuery
- Cloud SQL
Job areas
- Technology
- Software
- Data & Analytics
- Engineering