Full Stack Solution Architect – GenAI & Cloud

Job summary

The architect will design and deliver enterprise-scale GenAI and agentic AI solutions across hybrid and multi-cloud environments. They will act as a technical advisor to engineering teams to define architectural target states and production-ready AI roadmaps.

Job details

Full Stack Solution Architect – GenAI & Cloud Location: Toronto, ON – Hybrid, currently 2 days onsite Duration: 24 months Positions: 2 Start: ASAP The Role We are seeking a senior Full Stack Solution Architect to design and deliver enterprise-scale infrastructure, cloud and AI solutions. This role will act as a technical advisor to infrastructure and engineering teams, defining architectural target states, solution patterns and roadmaps for complex initiatives. A major focus will be designing production-ready GenAI and agentic AI architectures across hybrid and multi-cloud environments. Key Responsibilities Design end-to-end enterprise GenAI, LLM and agentic AI architectures. Develop architecture for AI/LLM gateways, AI agents, RAG, vector search, knowledge retrieval and prompt orchestration. Design model access and consumption patterns, including MCP/tool integrations. Create high- and low-level solution designs and architecture blueprints. Architect solutions across Azure OpenAI, Azure AI Foundry, Google Vertex AI/Gemini and hybrid cloud environments. Design AI observability, evaluation, security guardrails and responsible AI controls. Establish patterns for identity/access management, data protection, model routing, semantic caching, resiliency and production support. Integrate AI platforms with enterprise APIs, data platforms and cloud infrastructure. Partner with engineering, platform and executive stakeholders to translate business needs into scalable technical solutions. Must-Have Skills 10+ years of technology experience with extensive hands-on solution architecture experience. Demonstrated experience leading complex enterprise technology integrations. Extensive architecture experience delivering enterprise GenAI, LLM and agentic AI solutions. Hands-on architecture experience with AI agents, AI/LLM gateways, RAG, vector search, prompt orchestration and model consumption patterns. Strong understanding of MCP/tool integration. Deep AI/cloud platform architecture experience, including Azure OpenAI, Azure AI Foundry and/or Google Vertex AI/Gemini. Experience with AI observability, evaluation, responsible AI guardrails and model lifecycle management. Strong knowledge of IAM, data protection, prompt/response security, token and cost governance, logging, tracing and auditability. Experience designing scalable, resilient and production-ready solutions across cloud and hybrid environments. Undergraduate degree required. Strong communication skills with the ability to engage technical teams and senior/executive stakeholders. Nice to Have Experience within banking, financial services or another regulated environment. CI/CD, pair programming and/or Test-Driven Development experience. AI, cloud, architecture, security, data, MLOps, Kubernetes, API management or enterprise architecture certifications. Graduate degree. Note: We use AI tools to: obtain basic information, detect plagiarism, false employment history or references, categorize your skills, and do an initial match with job posting.

What you’ll do

The architect will design and deliver enterprise-scale GenAI and agentic AI solutions across hybrid and multi-cloud environments. They will act as a technical advisor to engineering teams to define architectural target states and production-ready AI roadmaps.

Requirements

Candidates must have over 10 years of technology experience with extensive hands-on solution architecture in GenAI, LLM, and cloud platforms. An undergraduate degree is required, along with strong communication skills to engage executive stakeholders.

Listed skills

• Kubernetes · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Solution Architecture
• GenAI
• LLM
• Agentic AI
• Azure OpenAI
• Google Vertex AI
• RAG
• Vector Search
• Prompt Orchestration
• Cloud Infrastructure
• IAM
• Data Protection
• AI Observability
• Responsible AI
• Enterprise Integration
• Kubernetes

Job areas

• Technology
• Software
• Data & Analytics
• Consulting
• Engineering