AI Solutions Architect

Are you an AI Solutions Architect who has actually taken AI into production ?

Have you designed systems using LLMs, RAG, agents, vector search, and model orchestration beyond the POC stage?

Do you want to help shape how a growing organization builds and scales production-grade AI ?

We’re working with a Toronto-based organization making a significant investment in AI and looking for someone who can bridge AI architecture, engineering, data, cloud, and the business .

This is not simply a Software Engineer using AI tools to code. We’re looking for someone who has been responsible for architecting, integrating, deploying, and scaling AI systems in real production environments .

What You’ll Do Architect end-to-end GenAI and LLM-powered solutions from POC through production.

Design RAG pipelines, agentic workflows, model orchestration, and enterprise AI integrations .

Build architectures across LLM APIs, embeddings, vector databases, data pipelines, APIs, and cloud infrastructure .

Define approaches around model evaluation, observability, security, governance, and guardrails .

Make architectural decisions around latency, scalability, reliability, cost, and model performance .

Work across engineering, data, cloud, product, and business teams to turn AI use cases into production systems.

You Are Experienced delivering LLM / GenAI systems into production , not just prototypes.

Strong across RAG, embeddings, vector search, agents, prompt/model orchestration, and AI APIs .

Comfortable with AWS/Azure/GCP, APIs, data architecture, distributed systems, and modern software architecture .

Familiar with MLOps/LLMOps, evaluation frameworks, monitoring, AI security, and responsible AI practices .

Able to translate complex business requirements into scalable AI architecture.

Why This Role Real AI Engineering – production systems, not AI demos.

Architecture Ownership – influence how AI is designed, integrated, governed, and scaled.

Strong Compensation – up to $200K+ base .

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