# AI Engineer Intern at ShyftLabs

> Onsite · Canada · Intern · ML / AI · open. Source: https://alderhq.app/jobs/lever%3Ashyftlabs%3A4f389ea7-9b98-4ed0-99c2-b25ea8cc2dcd (read 2026-11-05).

## Facts (observed)

- Company: [ShyftLabs](https://alderhq.app/companies/shyftlabs)
- Location text: Toronto, Ontario
- Where it can be done from (inferred): Onsite · Canada (evidence: "Location: Toronto, Ontario")
- Posted: 2026-07-21 (107 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.lever.co/shyftlabs/4f389ea7-9b98-4ed0-99c2-b25ea8cc2dcd
- Skills listed (inferred): Python, PostgreSQL, Redis, AWS, Kubernetes, Docker, LLMs

## Pay and sponsorship

- Posted pay: CAD 42k–CAD 62k per year (from the description)
- Market band for similar posted roles (Toronto, n 23): p25 CAD 158k, median CAD 193k, p75 CAD 201k

## Description (the employer's text)

### What You'll Be Doing

Build and improve agent orchestration and multi-agent workflows.

Develop agentic applications for enterprise use cases using Continuum.

Work with different commercial and open-source models, including OpenAI, Anthropic, Gemini, Llama, Qwen, Mistral, and others.

Improve intelligent model routing based on task complexity, quality, latency, and cost.

Build persistent memory and state-management capabilities for long-running agent workflows.

Develop tool-calling functionality and integrations with APIs, databases, and enterprise systems.

Work with MCP servers and function tools to connect agents with external services.

Design context-engineering and retrieval pipelines using vector and graph databases.

Implement AI guardrails for safety, security, access control, data privacy, and policy enforcement.

Create evaluation pipelines to measure accuracy, groundedness, hallucination, tool usage, workflow completion, latency, and cost.

Improve the observability and traceability of agent decisions, tool calls, and workflow execution.

Optimize prompts, model usage, token consumption, response time, and infrastructure costs.

Build APIs, backend services, and user-facing prototypes for agentic applications.

Write clean, reusable, well-tested, and documented Python code.

Contribute to Continuum’s open-source codebase, examples, documentation, and developer experience.

### What We Are Looking For

Currently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.

Strong programming skills in Python.

Good understanding of machine learning, natural language processing, and LLM fundamentals.

Experience building at least one LLM-powered or agentic application.

Familiarity with prompt engineering, embeddings, RAG, tool calling, and structured outputs.

Experience working with APIs, Git, databases, and standard software engineering practices.

Ability to research complex technical problems, experiment with different approaches, and clearly communicate results.

Strong interest in building reliable AI systems, not just basic LLM demos

### Nice to Have

Experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or similar technologies.

Familiarity with OpenAI, Anthropic, Gemini, AWS Bedrock, or open-source models.

Experience with vector databases such as Milvus, Pinecone, Weaviate, or Chroma.

Experience with graph databases such as Neo4j.

Knowledge of PostgreSQL, Redis, Docker, Kubernetes, or cloud infrastructure.

Familiarity with AI observability and evaluation tools such as Langfuse.

Understanding of multi-tenancy, identity management, authorization, or enterprise security.

Experience with model routing, inference optimization, prompt compression, or cost optimization.

Contributions to open-source AI projects, research, hackathons, or technically strong personal projects.

### Hourly Pay

- $20 - $30/Hr (CAD)

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/lever%3Ashyftlabs%3A4f389ea7-9b98-4ed0-99c2-b25ea8cc2dcd and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.