Feeling lost while trying to break into AI/ML how should I focus my projects? [D]

I’m trying to break into AI/ML Engineer / Applied AI roles, and honestly I’ve been feeling pretty overwhelmed lately.

I’ve been building around LLM evaluation, model reliability, cost optimization, and production AI systems. My main projects are:

RDAB — a benchmark for evaluating LLM data agents beyond just correctness, including code quality, efficiency, and statistical validity.

CostGuard — an LLM reliability/cost proxy that tracks model cost, applies fallback logic, does lightweight response checks, and supports replay-based model comparison.

Tether — a trace capture layer that records LLM calls so they can be replayed against alternate models to compare quality and cost.

The overall idea is:
capture real LLM traffic → replay it against another model → compare quality, cost, and reliability before switching models.

But I’m struggling with how to package this clearly. I feel like I’ve built a lot, but I’m not sure what hiring managers actually care about or what would make this stand out in a competitive market.

Right now I’m thinking of focusing everything around one story:

“Can a cheaper LLM replace an expensive one without silently hurting quality?”

Then use CostGuard as the flagship project, with RDAB as the benchmark layer and Tether as the trace-capture layer.

For people working in AI engineering, ML platforms, LLM infra, or applied AI:

What would make this project stack more impressive or easier to understand?

Should I focus more on:

1. a polished demo video,
2. a case study,
3. better README/docs,
4. more technical depth,
5. more real-world examples,
6. or outreach/networking around it?

Any honest guidance would help. I’m trying to turn this into something that clearly shows production AI engineering ability, not just another AI demo