Design, build, and harden agentic workflows that plan and take actions reliably — and understand why agents fail (context loss, compounding errors, no feedback signal) and how to structure tasks so they succeed.
Build retrieval (RAG/search) pipelines that fetch the right client data and ground model outputs in it, integrated into core applications rather than demos.
Select the right model for each task — reasoning model vs. fast instruction model — and be able to justify the trade-off in latency, cost, and quality.
Engineer prompts and context structures appropriate to the model class, including knowing when reasoning models need framing rather than step-by-step hand-holding.
Write evaluations for AI features, because with non-deterministic models “it worked once” is not evidence that it works.
Connect AI tools to internal systems and data sources via APIs or MCP to power real client use cases.
Review and validate AI-generated code and automated workflows critically for correctness, security, and safety.
Collaborate with the Builder and the Integration & Data Engineer to deliver complete, working solutions, and document workflows, prompts, and integrations in Notion.

Currently enrolled or recently graduated from a Master’s program in Computer Science, Software Engineering, AI/ML, Information Systems, or a related field. Master’s program enrollment or completion is mandatory.
Strong, demonstrable hands-on experience with AI coding assistants and the Claude API or comparable model APIs — portfolio, GitHub, or live examples strongly preferred.
A working understanding of how modern LLMs and reasoning models behave: context windows, the difference between reasoning and instruction models, and when to reach for each.
Practical experience with at least one of: building an agentic workflow, building a RAG/retrieval pipeline, or integrating models via tool calling or MCP.
Excellent prompt-engineering and context-management skills.
Coding fluency in JavaScript/React and/or Python sufficient to build, evaluate, and fix AI-generated output.
An instinct for evaluation: you want to measure whether the AI is actually correct, not just plausible.
Excellent verbal and written communication skills within a cross-functional team environment. New graduates are encouraged to apply.

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

Key skills