AI Advisor | Google AI Accelerator Alum
AI Founder | Co-Founder & CEO at Krybe |


The exact time and date will be discussed after payment. We will reach out to you to organize all logistics and have a setup call. If you have any questions about the program, you can email us at hello@theaiinternship.com before enrolling.
AI engineering has changed. The bottleneck is no longer writing every line yourself it's knowing what to delegate to AI, how to supervise it, and how to prove the output is safe to ship.
Most engineers still use Claude Code like autocomplete. This workshop turns it into a teammate. In one intense day you'll work in a real, unfamiliar codebase: ship a merged pull request, build an internal agent that completes a real task end to end, and stand up an evaluation harness with a path into CI.
You leave with working artefacts and a team operating model not notes. This is not a lecture. It's a hands-on build day where you ship.
You'll walk away with
- A merged change in a real codebase, shipped with Claude Code
- A working internal agent that completes a real task end to end
- An eval harness wired toward CI, so model behaviour is regression-tested not just "does the code run."
So email us or book a call here.
From "I've tried Claude Code" to shipping a real change, a working agent, and an eval harness in one focused day.
Decide what's genuinely safe to delegate vs. what stays human
Recognise the failure modes that matter and the review norms that catch them
Build a delegation map you can apply to your own backlog
Context engineering: give the agent what it needs to act well
Agentic patterns, tool use, and MCP for real workflows
Onboard Claude Code to a real repository
Navigate code you've never seen, unguided
Make the change and open a pull request
Get it reviewed and merged
Scope an internal task worth automating
Build the agent with tools + MCP
Run it reliably on a real task, with failure modes handled
Test behaviour with an eval harness (LLM-as-judge / trajectory eval)
Set acceptance criteria before engineering builds
Wire a path into CI as an eval quality gate
Ship one real feature or agent with a harness wrapped around it
Capture guardrails, review norms, and where people stay in the loop as your team's practices doc
Safe to delegate vs. keep human · AI failure modes to watch · review norms that keep quality high
Context engineering · agentic patterns · tool use and MCP · onboarding Claude Code to a real repo
Navigate an unfamiliar codebase · make the change unguided · open and merge a pull request
Scope a task worth automating · build the agent end to end (tools + MCP) · run it on a real task
Test behaviour, not just code that runs · stand up a harness (LLM-as-judge / trajectory eval) · wire a path into CI
Ship one real feature or agent · wrap an evaluation harness around it · demo with evidence
Guardrails · review norms · where people stay in the loop · your team's practices doc

AI Advisor | Educator | Google AI Accelerator Alum

AI Founder | Co-Founder & CEO at Krybe | Co-Founder of Snapdrum
Software engineers who want to use Claude Code as a real engineering teammate, not autocomplete
AI engineers shipping agents and AI features who need evals and guardrails
Tech leads & eng managers defining how their team works with AI delegation, review, quality
Live sessions
Learn directly from Aki Wijesundara, PhD & Manu Jayawardana in a real-time, interactive format.
Lifetime access
Revisit all lessons, recordings, templates, and code repositories whenever you need , your learning doesn’t expire.
Community of peers
Join a cohort of ambitious builders, stay accountable, and collaborate with engineers, PMs, and founders working on similar goals.
Certificate of completion
Show your new AI engineering skills to employers, add it to LinkedIn, and strengthen your portfolio.
Follow-up office hours
Open office hours for questions and personalized feedback.
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Reimbursement
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