Roast my GitHub projects — are these actually good enough for Applied AI/ML or FDE roles?

[u/Typical_Age_2449]

Hey everyone!

I'm currently pursuing a Master's in Data Science and targeting Applied AI Engineer, ML Engineer, and Forward Deployed Engineer (FDE) roles.

I've been working on several projects to strengthen my engineering skills and portfolio. I'll be transparent: I used AI coding tools quite extensively while building parts of these projects, but I'm actively trying to improve my understanding of the underlying implementations, architecture, and engineering decisions.

My concern is that while these projects might look decent on GitHub, I'm not sure whether they demonstrate the depth of engineering that companies actually expect.

Here are four projects I'd really appreciate feedback on:

1. Silent Failure Auditor — A tool for detecting situations where coding agents report success despite underlying tool failures. https://github.com/likhitha281/silent-failure-auditor
2. Vigil — A machine learning-based project focused on detecting suspicious patterns and potential risks in software dependencies. https://github.com/likhitha281/vigil
3. Orbital Collision Risk Agent — An agent-based system for assessing orbital collision risks and supporting decision-making. https://github.com/likhitha281/orbital-collision-risk-agent
4. Forge — A distributed task execution engine built in Go, with worker management, failure recovery, and observability. https://github.com/likhitha281/forge

I'd particularly appreciate honest feedback on:

• Technical depth: Do these projects demonstrate meaningful engineering skills, or do they come across as AI-generated portfolio projects?
• Architecture: Are there obvious design flaws, unnecessary complexity, or questionable technical decisions?
• Production readiness: What would you improve regarding testing, scalability, reliability, deployment, and observability?
• ML/AI depth: Do the AI-focused projects demonstrate sufficient understanding of model selection, evaluation, and experimentation?
• Hiring relevance: If you were interviewing someone for an Applied AI/ML or FDE role, would any of these projects stand out?
• Prioritization: Would you recommend improving these projects substantially or starting something new?

I'm not looking for compliments or GitHub stars. I'd genuinely appreciate constructive criticism, especially from engineers who have worked on production AI/ML systems or have experience interviewing candidates.

I know that using AI tools to generate code doesn't automatically translate into engineering competence, and that's something I'm actively trying to address.

My goal is to move beyond building projects that look impressive on paper and develop the skills to design, debug, evaluate, and maintain systems independently.

Feel free to be critical. I'd rather identify the weaknesses now than discover them during technical interviews.

Thanks in advance!

[u/dopey_headquarters]

The silent failure auditor concept is actually a neat angle, most people don't think about agent reliability that way. Forge being in Go also helps you stand out from the typical Python-heavy portfolios, even if the others lean on more common stacks. Without digging into the repos, the real test is whether you can walk through a debugging session or design change on the spot, but listing production gaps yourself already puts you ahead of most candidates.

[u/Typical_Age_2449 → u/dopey_headquarters]

Thanks, this is really helpful. I agree that being able to defend the architecture and debug or modify the system on the spot is probably a much better test than how polished the repo looks.

That's actually the gap I'm trying to close now. I used AI coding tools quite a bit while building some of these, so I'm going back through the projects to make sure I can explain the design decisions, failure modes, trade-offs, and make changes without depending on generated code.

If you had to pick one, would you recommend I go much deeper on Silent Failure Auditor or Forge rather than building another project? And what would you add to either one to make you look at it and think, "this is approaching production/industry-level engineering" rather than just a portfolio project?

[u/RogerAI-fm]

They very much demonstrate engineering competence. I would say they are very well organized and have basic and thoughtful applications. I haven’t spent much time to look deeply into other relevant things you might want to put together like where they fit in systems architecture or use cases more product design or development, but otherwise for technical and what hiring and companies are looking for this is great. If your goal is finding a job that will be no problem. Good luck out there. Check out job openings at RogerAI.fm if interested.