AI Engineer Intern

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.

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

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

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

What We Are Looking For

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

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Strong programming skills in Python.

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

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

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

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

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

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

Nice to Have

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

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

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

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Experience with graph databases such as Neo4j.

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

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Familiarity with AI observability and evaluation tools such as Langfuse.

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

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

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

Hourly Pay

• $20 - $30/Hr (CAD)