Role descriptionPosition Title- AI ArchitectLocation- Toronto, ONWe 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
- Google Cloud Platform (GCP): Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Cloud SQL, Pub/Sub, Dataflow, GKE (Google Kubernetes Engine)
- Vector Databases & Search: Vertex AI Vector Search, Pinecone, ChromaDB, FAISS, pgVector
- AI Frameworks & Development: LangChain, LangGraph, LlamaIndex, FastAPI, REST APIs, Python, SQL
- 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.
Cloud & Platform Engineering- 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.
Leadership & Collaboration- 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.
Location: Toronto, ON
Work Mode: Hybrid, 3-4 days per week
