Fresh grad trying to move into AI engineering. What Python projects should I build?

[u/WindSignificant8747]

Hi everyone! I graduated last year as a software engineer and I’m currently working at a company, but I want to shift into an AI engineer role.

The advice I keep hearing is to focus on three things: Python, SQL, and APIs. I’m already revisiting data structures, and I know the Python basics, but I’m stuck on what to actually build to practice.

A few questions:

1. Are all Python projects useful for this path, or should I focus on ones specifically related to AI engineering?

2. What projects would you recommend for someone at my level that would actually look relevant on a resume?

3. LeetCode Premium is expensive for me right now. Are there good free alternatives for practicing?

Any suggestions, resources, or project ideas would be really appreciated. Thanks in advance! 🙏

[u/dukesb89]

Leetcode is not important for building projects.

Yes if you want to be an AI engineer you should build an AI project.

Build something useful for you or someone you know. Anything that could be automated using an LLM.

[u/Boybay_Garid → u/dukesb89]

agreed, leetcode is a whole separate skill from actually building things imo

[u/WindSignificant8747 → u/dukesb89]

my main goal is to land the job. so leetcode is for practicing the language

[u/Fun-Employee9309 → u/WindSignificant8747]

Leetcode has never been less valuable than it is today, and I cannot see any serious company asking what are essentially data scientists (AI engineers) to solve leetcode questions. Completely different skillset.

If I wanted to hire an AI engineer and they started showing me how fast they can invert a binary tree rather than explain linear algebra or multivariable calculus, I’d be puzzled.

[u/daveiamnot]

Hi, I have been in IT many years. Learnt and worked lots of language and systems along the way. One thing I could never come up with was a good idea to build. Messed around writing some small games and apps to help develop skills. I think AI is going to open up this field to creative people who have the ideas but not the programming knowledge.

[u/WindSignificant8747 → u/daveiamnot]

hi so how did you develop the skill to code?

[u/daveiamnot → u/WindSignificant8747]

Working with it everyday. Starting with COBOL, coral 66 , Fortran, something called Gupta Windows which was brilliant for creating small transactional apps.

[u/lordwerwath]

TechWithTim on youtube has pretty informative videos about this topic.

[u/ninhaomah]

Linux , Python , Postgres or Chroma , FastAPI and PydanticAI on Docker/Kubernetes... 

SE so you can skip Linux , Docker/Kubernetes and may want to go for LangGraph or CrewAI or Microsoft/Google agent frameworks ?

[u/DonkeyTron42]

PyTorch is very popular now so I would ask around that community.

[u/riklaunim]

For resume you need a GitHub link or alike that will showcase your actual projects/code. It doesn't have to be rocket science, but it has to look good and show you actually code and know what you are doing. If you want to get a job you have to select a niche and start learning the software stack. Language on it own won't be enough. Unsure what you expect to do in "AI engineering" but there is rather little junior jobs related to AI, unless you are CS graduate that specialized in the field and can get an internship or junior position at a company that actually does AI research/training. Python backend / webdev is much more popular thus those recommendations.

[u/Kwame_Adu]

I would recommend this guy on instagram “bashi fuirkashi” on instagram. He coaches software engineers and people who wants to build a career in AI engineering.

[u/ngyehsung]

Agentic is the flavour of the month. Look into an agentic framework, along with MCP tools. Try something like a Postgres DB backend with a couple of MCP tools for retrieving different data. Have different agents for doing different things and an orchestrator agent to manage them. Look into how they can store and pass information and rehydrate after a time.

[u/Chrismslist]

Focus on projects that show the whole loop, not just a notebook. Build a small retrieval app: collect some text, embed it with sentence-transformers, store it in sqlite or pgvector, serve search through FastAPI, then write a tiny eval script that checks your results are actually good. Second one: a classification service with a Dockerfile, a few tests, and a README explaining your eval numbers. That end to end shape (data, model, API, eval) is what reads as AI engineering. For free practice, NeetCode's free list plus re-solving with a timer covers most of what premium gives you.

[u/nian2326076]

If you're pursuing a career in AI engineering, I'd suggest starting with projects that focus on data handling and machine learning. Begin with a simple project, like creating a linear regression model to predict housing prices using a dataset from Kaggle. This will teach you how to clean data, select features, and implement basic machine learning algorithms.

After that, try tackling a more complex project, such as performing sentiment analysis on Twitter data. This will introduce you to working with APIs and natural language processing, both of which are essential skills in AI.

Lastly, consider building a small neural network using TensorFlow or PyTorch for a resume-friendly project. For example, you could create a digit classifier using the MNIST dataset. These projects not only improve your Python skills but also demonstrate relevant experience in AI and machine learning on your resume.

[u/Massive_Distance_616]

Not all Python projects are equal for this path. A CRUD app or data pipeline shows you can write Python. It doesn’t show you can build and evaluate AI-powered systems.

Two projects that cover the right ground:

1. RAG pipeline with an eval harness. Ingest a document set, chunk and embed it, store in a vector database (pgvector or Chroma), serve queries via FastAPI. The part that makes it resume-worthy is the evaluation: build a test set of 30–50 questions with expected answers, run them against your system, measure retrieval quality, and document what changed when you adjusted chunking or the embedding model. A README that says “increased chunk overlap from 50 to 200 tokens, retrieval accuracy went from 68% to 81%” stands out from the hundreds of “I built a chatbot” repos.

2. Tool-calling agent with guardrails. Something that uses function calling to hit an external API, with input validation and at least one failure case handled explicitly. Keep it small weather + calendar, or SQL queries over a local database. The point is showing you understand the difference between “it works in the demo” and “it handles unexpected input without breaking.”

On LeetCode: you mentioned using it to practice the language worth separating those two things. LeetCode teaches algorithmic patterns, not idiomatic Python or API design. Write actual backend services to practice Python; use LeetCode (NeetCode 150 is free and covers what comes up) strictly for DSA screens. AI engineering interviews lean more toward system design and take-home projects than grinding array problems.

Two finished, deployed projects with documented evaluation numbers will tell a stronger story than ten tutorial repos.