Applied AI Engineer – Agentic Workflows, RAG – Master-Level Internship

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design, construct, and enhance agentic workflows that effectively plan and execute actions reliably — while understanding the reasons behind agent failures (such as context loss, compounding errors, and lack of feedback signals) and how to structure tasks to ensure their success.

• Develop retrieval (RAG/search) pipelines that accurately obtain the necessary client data and ground model outputs within core applications rather than mere demonstrations.

• Choose the appropriate model for each task — distinguishing between reasoning models and fast instruction models — and be prepared to articulate the trade-offs in latency, cost, and quality.

• Create prompts and context structures that are suitable for the model class, including recognizing when reasoning models require framing instead of detailed step-by-step guidance.

• Compose evaluations for AI features, as with non-deterministic models, “it worked once” does not constitute evidence of consistent performance.

• Integrate AI tools with internal systems and data sources through APIs or MCP to facilitate real client use cases.

• Critically review and validate AI-generated code and automated workflows for correctness, security, and safety.

• Work collaboratively with the Builder and the Integration & Data Engineer to deliver comprehensive, functional solutions, and document workflows, prompts, and integrations in Notion.

⛳️ Requirements

• Currently enrolled or recently graduated from a Master’s program in Computer Science, Software Engineering, AI/ML, Information Systems, or a related field. Enrollment or completion of a Master’s program is essential.

• Strong and demonstrable hands-on experience with AI coding assistants and the Claude API or similar model APIs — portfolio, GitHub, or live examples are highly preferred.

• A solid understanding of how modern LLMs and reasoning models function: context windows, the distinctions between reasoning and instruction models, and when to utilize each.

• Practical experience in at least one of the following: developing an agentic workflow, constructing a RAG/retrieval pipeline, or integrating models through tool calling or MCP.

• Exceptional prompt-engineering and context-management abilities.

• Proficient coding skills in JavaScript/React and/or Python sufficient to build, assess, and rectify AI-generated outputs.

• An innate ability for evaluation: a desire to measure whether the AI is genuinely accurate, rather than just plausible.

• Outstanding verbal and written communication skills in a cross-functional team setting. New graduates are encouraged to apply.

🏝️ Benefits

• Competitive salary and opportunities for advancement.

• Flexible working hours and the option for remote work.

• Access to cutting-edge technology and resources.

• Collaborative and inclusive work environment.

• Professional development and training opportunities.