Posted 2 months ago
AI Architect- Hybrid
@ Staffmax
Toronto, Ontario
Overview
Staffmax is hiring a temporary AI Architect in Toronto, ON, on a hybrid arrangement, at a stated salary of $120K. The role designs enterprise Generative AI and Agentic AI solutions on Azure AI Foundry and Databricks, builds prototypes and reference implementations, and sets governance, Responsible AI and architecture standards.
Responsibilities:
- Design enterprise Generative AI and Agentic AI solutions on Azure AI Foundry and Databricks
- Build prototypes, proofs of concept and reference implementations
- Define AI governance, model lifecycle management and compliance controls
- Guide engineering teams on architecture reviews and best practices
Requirements summary:
- Experience designing scalable Agentic AI architectures
- Knowledge of multi-agent orchestration and tool integration
- Understanding of Responsible AI principles and AI governance
- Ability to translate business requirements into AI solutions
Tools & skills
Source and dates
Employer career site / ATS feed · careerpage
- Employer posting date
- 2026-09-08
- First seen by our source
- 2026-10-11
- Source record updated
- 2026-10-11
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Software development |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Staffmax
| Field | Value |
|---|---|
| Open jobs on Jobsy | 150 |
Location: Toronto, ON – Hybrid
Salary: $120K
Key Responsibilities
- Design and architect enterprise-grade Generative AI and Agentic AI solutions using Azure AI Foundry and Databricks.
- Develop hands-on prototypes, Proof of Concepts (PoCs), and reference implementations to validate business use cases.
- Create and implement scalable Agentic AI architectures, including multi-agent orchestration, tool integration, planning, reasoning, and autonomous workflow execution.
- Define reusable Agentic AI frameworks, accelerators, and patterns that can be leveraged across multiple business domains.
- Lead AI solution deployment strategies, ensuring scalability, reliability, security, and maintainability.
- Establish AI governance frameworks, model lifecycle management, auditing, and compliance controls.
- Drive Responsible AI enablement by incorporating fairness, transparency, privacy, security, and ethical AI principles into solution designs.
- Collaborate with business stakeholders, data scientists, engineers, and product teams to translate business requirements into AI solutions.
- Define AI architecture standards, best practices, and technical guidelines for enterprise adoption.
- Implement observability, monitoring, performance optimization, and risk management for GenAI applications.
- Guide engineering teams on AI development best practices, architecture reviews, and technical governance.
Journal
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