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Full Stack AI Engineer

Job in Toronto, Ontario, C6A, Canada
Listing for: Jobgether
Full Time position
Listed on 2026-09-01
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Full Stack Developer, Backend Developer, Software Architect
Salary/Wage Range or Industry Benchmark: 140000 - 210000 CAD Yearly CAD 140000.00 210000.00 YEAR
Job Description & How to Apply Below
Position: Staff Full Stack AI Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Full Stack AI Engineer based in Canada.

This is a senior individual-contributor opportunity at the intersection of full-stack engineering, artificial intelligence, and digital product development.
You will design and deliver production-grade applications spanning modern front‑end, back‑end, cloud, and AI technologies.
The role combines hands‑on engineering with technical leadership across complex, ambiguous client engagements.
You will build AI‑powered capabilities including LLM integrations, RAG systems, agentic workflows, and intelligent automation.
You will influence architecture, engineering standards, delivery practices, and the adoption of emerging AI technologies.
Working closely with clients and multidisciplinary teams, you will turn business challenges into scalable, reliable software solutions.

Accountabilities
  • Work directly with client teams to discover, scope, architect, and deliver full‑stack applications, taking ownership from ambiguous requirements through production.
  • Design, develop, and deploy scalable web applications using technologies such as React, Next.js, Type Script, modern back‑end frameworks, and cloud platforms.
  • Identify, prototype, and product ionize AI capabilities, including LLM integrations, retrieval‑augmented generation (RAG), agentic workflows, tool/function calling, and intelligent automation.
  • Build AI solutions with appropriate evaluation frameworks, guardrails, security considerations, and awareness of model cost and latency trade‑offs.
  • Use AI‑assisted development tools to increase engineering velocity while maintaining strong standards for quality, reliability, and maintainability.
  • Implement infrastructure‑as‑code and automated deployment pipelines to create scalable, observable, and dependable solutions.
  • Develop automated testing strategies, including unit, component, integration, and AI evaluation testing, to ensure robust production outcomes.
  • Conduct thorough code reviews, establish engineering best practices, and maintain high technical standards in fast‑moving, client‑facing environments.
  • Provide technical leadership and mentorship, helping engineers and clients strengthen their full‑stack and AI capabilities.
  • Manage delivery expectations with stakeholders, balancing scope, quality, timelines, technical trade‑offs, and engagement constraints.
  • Shape technical direction across teams and engagements, documenting architectural decisions and the trade‑offs behind them.
  • Stay current with rapidly evolving AI and web technologies and develop informed perspectives on where emerging tools and models can create meaningful business value.
Requirements
  • 7+ years of experience working on substantial software development projects, with strong expertise in React and modern front‑end engineering.
  • Advanced Type Script skills and strong knowledge of modern React concepts, including component life cycles, hooks, concurrent features, Server Components, and Next.js App Router patterns.
  • Experience with state‑management approaches such as Redux, Context, and RTK Query, with an understanding of when to apply each.
  • Strong testing experience using tools such as Jest, Vitest, React Testing Library, Cypress, or comparable frameworks.
  • 5+ years of experience developing and integrating REST APIs and back‑end services using technologies such as Node.js, .NET, Ruby on Rails, Go, or similar.
  • Hands‑on experience with a major cloud platform such as AWS, Azure, or GCP, together with infrastructure‑as‑code practices.
  • Demonstrated ability to architect complex software systems, make sound design decisions, and clearly document technical trade‑offs.
  • 2+ years of practical experience building modern AI capabilities that have been delivered into real products or client engagements.
  • Experience integrating LLMs through major provider APIs such as OpenAI, Anthropic, or equivalent platforms.
  • Working knowledge of RAG, vector databases, embeddings, prompt engineering, agentic architectures, orchestration frameworks, and tool/function calling, including protocols such as MCP.
  • Understanding of AI…
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