We are seeking a highly experienced AI Architect to lead the design, architecture, and implementation of enterprise-scale Generative AI solutions on Google Cloud Platform (GCP) . The ideal candidate will possess deep expertise in Retrieval-Augmented Generation (RAG) , Large Language Models (LLMs), Agentic AI, Vector Databases, and the Google AI ecosystem including Vertex AI and Gemini . The role requires a blend of strategic architecture leadership and hands-on technical expertise to deliver scalable, secure, and production-ready AI platforms. Years of Experience: 10+ years of overall experience in software engineering, cloud architecture, or data platforms. 5+ years of experience designing and implementing AI/ML solutions. 3+ years of experience delivering Generative AI and LLM-based applications in enterprise environments. Proven experience implementing RAG architectures , conversational AI platforms, and AI-powered knowledge management solutions. Technical Skills: Required: Bachelor’s degree in Computer Science, Data Science. Generative AI & LLMs: Gemini, Vertex AI, OpenAI, Llama, Foundation Models, Prompt Engineering, Conversational AI, Agentic AI / Multi-Agent Architectures, AI Model Evaluation and Monitoring RAG & Knowledge Systems: Retrieval-Augmented Generation (RAG), GraphRAG, Knowledge Graphs, Semantic Search, Embeddings and Vector Search, Document Intelligence and Enterprise Search MLOps & DevOps: Vertex AI Pipelines, MLflow, CI/CD for AI Applications, Model Governance & Monitoring, Infrastructure as Code (Terraform) Security & Governance: Responsible AI, AI Risk Management, Data Governance, Security Architecture, Compliance & Audit Controls Preferred Certifications: Google Cloud Professional Cloud Architect Google Cloud Professional Machine Learning Engineer Google Generative AI Certifications Databricks Generative AI Certifications (preferred) Key Responsibilities: AI Architecture & Strategy Define enterprise AI architecture standards,



patterns, and best practices. Design end-to-end Generative AI, RAG, and Agentic AI solutions. Develop AI roadmaps aligned with business objectives and technology strategy. RAG & Knowledge Platform Design Architect large-scale RAG and GraphRAG solutions. Design document ingestion, chunking, indexing, retrieval, re-ranking, and grounding strategies. Optimize AI solution accuracy, scalability, latency, and cost efficiency. Build enterprise knowledge platforms leveraging structured and unstructured data sources. Generative AI Solution Delivery Lead the design and implementation of AI assistants, chatbots, copilots, and autonomous agents. Enable integration of LLMs with enterprise systems, APIs, and workflows. Establish frameworks for prompt engineering, model evaluation, and continuous improvement. Architect scalable AI platforms using GCP services. Drive cloud-native AI application development and deployment. Define best practices for performance optimization, reliability, observability, and resilience. Governance, Security & Responsible AI Implement AI governance frameworks, security controls, and monitoring capabilities. Ensure compliance with enterprise policies, data privacy, and regulatory requirements. Establish standards for model transparency, explainability, and risk management. Partner with business stakeholders, product owners, data engineers, and AI teams. Conduct architecture reviews and technical design workshops. Mentor engineering teams and promote AI adoption across the organization. Present architecture recommendations and investment strategies to executive leadership. We are an Equal Prospect Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law. #J-18808-Ljbffr

📌 Agentic Ai Architect (Toronto)
🏢 AI Labb
📍 Toronto