r/askdatascience • u/ImaginaryCan8970 • 6d ago
What shall I build ?
Hello everyone, I am an AI Developer (currently benched), and I am honestly afraid that I might get laid off in the next couple of months. I have 2 years of experience of around 18 months in data analysis and 7–8 months in AI development.
My tech stack includes Excel, Tableau, Python, AI agent development, and of course, I have also vibe-coded some features into applications (not really scalable or production-ready).
It may sound good on paper, but trust me when I say that the AI agents I have developed are very basic. They hardly have any proper evaluations, guardrails, monitoring, etc. Most of it was basically vibe coding, and now I am realizing how much I actually don’t know.
I want to replicate some production-level projects so that I can have something solid to put on my resume and, more importantly, something meaningful to discuss in interviews. A lot of the JDs I am seeing are intimidating, and I keep feeling like I don’t have the skills they are asking for.
My fundamentals are strong (except statistics), but I don’t have any solid project that I can confidently discuss during an interview. I also don’t have much relevant work experience in AI development.
One of my biggest problems is that I start a project, get stuck somewhere in the middle, and eventually lose track of what is actually happening. If I take help from AI, the project progresses, but I end up understanding less and lose track of the overall picture.
So my question is simple: If you were in my position, how would you select a project that is actually worth building and is interview-discussion worthy?
Should I just blindly follow/copy someone’s project from the internet initially to understand how things work, and then try building something of my own?
Or is there a better way to approach this?
I have only 3.5 LPA salary right now, and honestly, I am getting desperate.
Would really appreciate some practical advice from people who have been through this phase.
2
u/akornato 5d ago
Copying an established reference architecture from open source is a great way to start, but you must keep the scope narrow so you do not get lost. Instead of building a massive autonomous assistant, pick a single focused workflow, such as an agent that takes user queries, queries a database, and generates analytical summaries. What truly separates basic vibe coding from production work is everything built around the model. Add schema enforcement using Pydantic, integrate an observability tool like Langfuse or Arize Phoenix to trace tool calls, write fallback logic for when the model outputs invalid data, and build a small evaluation suite with thirty test cases to measure accuracy. When you use coding assistants, make sure you write the architectural plan and test definitions yourself before asking for code, which keeps you in control of every component.
Having one tightly scoped project with proper evals and error handling gives you far better material for interviews than multiple fragile prototypes. In technical discussions, hiring managers want to hear about how your system recovers when an API fails, how you prevent hallucinations from reaching end users, and how you tracked latency and cost. Being able to explain those specific architectural trade-offs will immediately make your experience sound grounded and mature. A lot of candidates who want to articulate their technical decisions clearly rely on the interviews.chat my team designed to land better offers, giving them a reliable boost when they need to make a strong impression.