AI Engineer
CA$20 – CA$30 / hr base salary
Posted 21 Jul 2026
Advertised as “AI Engineer Intern”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Internship
| Base salary | CA$20 – CA$30 / hr |
|---|
About ShyftLabs
At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth through innovative digital solutions that drive real business impact. With teams across Canada, the U.S., and India, we’re growing quickly and looking for curious, motivated individuals who are excited to learn, build, and solve meaningful problems with technology.
About Continuum
Continuum is an enterprise AI agent execution and control platform designed to help teams build, run, and deploy reliable agentic applications. It provides agent orchestration, multi-model routing, persistent memory, tool integration, durable workflows, governance, guardrails, evaluation, and observability.
We are looking for an AI Engineer Intern interested in building production-ready AI agents and applications. You will contribute to the Continuum platform while also using it to build agentic applications for real enterprise use cases.
This is a hands-on engineering role. You will work across agent orchestration, memory systems, model optimization, retrieval, tools, guardrails, evaluation, and application development.
What You'll Be Doing
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Build and improve agent orchestration and multi-agent workflows.
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Develop agentic applications for enterprise use cases using Continuum.
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Work with different commercial and open-source models, including OpenAI, Anthropic, Gemini, Llama, Qwen, Mistral, and others.
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Improve intelligent model routing based on task complexity, quality, latency, and cost.
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Build persistent memory and state-management capabilities for long-running agent workflows.
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Develop tool-calling functionality and integrations with APIs, databases, and enterprise systems.
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Work with MCP servers and function tools to connect agents with external services.
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Design context-engineering and retrieval pipelines using vector and graph databases.
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Implement AI guardrails for safety, security, access control, data privacy, and policy enforcement.
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Create evaluation pipelines to measure accuracy, groundedness, hallucination, tool usage, workflow completion, latency, and cost.
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Improve the observability and traceability of agent decisions, tool calls, and workflow execution.
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Optimize prompts, model usage, token consumption, response time, and infrastructure costs.