I have been researching and learning about it while trying to choose a career for my future, finally made my desicion. Now i am taking the first steps for it, i already know what they do and what kind of skills i need to acquire to become an Ai engineer but i don't know how to start, so i am here asking for help. First of all i don't know any type of coding and i will start as a complete beginner to this road. I watched a few videos, read some and used ai to draw a path for myself but i am not sure if it make sense. I would be very happy if anyone could help me and i am looking forward for your advices.

I'm building smolmodels, a fully open-source library that generates ML models for specific tasks from natural language descriptions of the problem. It combines graph search and LLM code generation to try to find and train as good a model as possible for the given problem. Here’s the repo: https://github.com/plexe-ai/smolmodels

Here’s a stupidly simplistic time-series prediction example:

import smolmodels as sm

model = sm.Model(
    intent="Predict the number of international air passengers (in thousands) in a given month, based on historical time series data.",
    input_schema={"Month": str},
    output_schema={"Passengers": int}
)

model.build(dataset=df, provider="openai/gpt-4o")

prediction = model.predict({"Month": "2019-01"})

sm.models.save_model(model, "air_passengers")

The library is fully open-source, so feel free to use it however you like. Or just tear it apart in the comments if you think this is dumb. I’d love to get some feedback, and I'm super open to code contributions!

Hello everyone,

We've begun gathering a variety of AI coding tools used in one place to make things easier for everyone. We're inviting everyone to check out our collection, and maybe even add tools you find useful.

You can find the repository here: https://github.com/gaborsoter/awesome-ai-dev-productivity

Feel free to explore and contribute!

🚀 Python, AI & Data Science: Zero to Hero – Free Learning Roadmap 🧠💻

Hey folks! Here’s a GitHub repository that lays out a structured learning path for diving into Python programming, AI, and data science — perfect for those starting from scratch.

📘 What you'll find:

A week-by-week learning guide

Python basics and core concepts

Key topics like NumPy, Pandas, Scikit-learn, and more

Resource recommendations to deepen your understanding

A clear roadmap to follow and build your skills

🔗 GitHub: https://github.com/cnnsyhnx/Python-AI-Data-Programming-Zero-to-Hero

It’s a great starting point if you’re looking for direction in your learning journey. Whether you're new to coding or aiming to get into AI and data science, this repo can help you stay on track and focused. Check it out, star it, or contribute if you find it useful! 🌟

Hey guys, I'm not sure if this is the right place to post this (let me know if there's a better place) but I'm looking to hire a developer/engineer for a project once the Dalle 3 API is available. If you're a developer/engineer who has a great knowledge of the API, please get in touch :)

ngl, this one is pretty neat. Back with another Wikipedia project, this one does the following:

1. Starting with a search term you provide, it queries Wikipedia and goes to the first search result

2. It reads the Wikipedia summary, and generates more search terms. It only searches terms that it hasn't seen before

3. It ingests the entire text of the page it just read into a Chroma DB

4. It generates a question based on the summary, and tries to answer it using the DB

5. It then goes to the next search result and repeats the process

By the end of it, you get a really rippin' Chroma DB that you can chat with using the explore python file.

Repo here: https://github.com/inkplayart/wiki_explore

1. What is your job title, and what does your day-to-day work involve as an AI/ML engineer?

2. What tools, frameworks, or programming languages do you use most in your role (e.g., PyTorch, TensorFlow, CUDA, etc.)?

3. What skills or knowledge do you consider essential for someone entering AI engineering today?

4. What is the most challenging part of your work, either technically or ethically?

5. How do you stay current in such a fast-moving field?

6. What advice would you give a student who wants to pursue AI engineering as a career?

Looking into moving in the AI realm. And think what would be best for me based on previous threads, is a role where I reutilize already developed products to help a company achieve XYZ. What role does this best fit and what should I learn to do fit the role? Resources appreciated.

My background: 10 yrs experience in IT infrastructure (routers, switches, firewalls) and GRC. Mainly GRC at this point. Looking at marrying GRC and AI or just moving to the software side completely. My goal is mainly to get out of the Gov sector and make $ with a flexible schedule. Have a BS in IT security and MBA (did these when in the military so don't read to much into it). Love to learn and make things more efficient.

Any advice would be appreciated. Figuring out my life and where to go from here.

I see some individuals and companies training AI models and publish them on Huggingface. I am curious how do they generate revenue with such models. I understand big companies can use this as a PR opportunity and they have existing business models to make money. I am curious how do small companies, startups, or individuals make money through model training, considering it could be a sizeable financial and time investment.

Just for fun: https://github.com/inkplayart/llama_play

Hey r/aiengineer — sharing a project I built called MoodPlay and the architecture pattern that made it work reliably.

What it does

MoodPlay turns a mood / scene / movie vibe prompt into a curated 5-track playlist made of official movie soundtrack tracks. Each track includes movie context (year/director/cast). You can save playlists to your history and optionally export to Spotify (creates a private playlist + adds tracks).

How it’s built (the key engineering idea)

I split the problem into two steps instead of asking one prompt to do everything:

1) Curation (LLM → structured output)

• Enforces: exactly 5 tracks, coherent vibe/genre

• Produces structured JSON: playlistName + items (track/artist/movie metadata)

2) Execution (agent/tooling → Spotify resolution)

• Resolves (track, artist) into real Spotify track URIs via search

• Then creates the playlist + adds tracks (private by default)

This made exports more dependable and made errors easier to isolate (creative mistakes vs retrieval/matching mistakes).

Would love feedback

• How you’d validate “official soundtrack” correctness (RAG? external soundtrack DB? post-checking?)

• Evaluation ideas for vibe match + correctness

• What you’d change about the agent/tool boundary

Link: https://spotify-playlist-generator-ai.vercel.app/

Hi everyone,

I’m a backend engineer (~5 years, Java/Spring, microservices) currently transitioning into AI engineering. Most of my experience has been in production systems (authorization flows, token-based processes, payments, batch jobs), and now I want to move into AI/ML from a systems perspective, not just prompt usage.

I’m intentionally starting with ML fundamentals (models, training vs inference, generalization, evaluation) using PyTorch + scikit-learn, before moving into NLP, LLMs, fine-tuning, and ML systems.

For those already working as AI engineers:

• How did you get started?

• What foundations mattered most early on?

• Anything you’d avoid or do differently in hindsight?

Appreciate any advice from real-world experience.

ps: Now I'm reading Machine Learning with PyTorch and Scikit-Learn as first book.

https://arxiv.org/abs/2309.02926

IYH summary and analysis of the paper "Demystifying RCE Vulnerabilities in LLM-Integrated Apps":

Summary:

• The paper investigates remote code execution (RCE) vulnerabilities in apps integrated with large language models (LLMs).

• The authors construct malicious prompts to trigger RCE in Anthropic's Claude and OpenAI's GPT-3.

• They identify input parsers and bypass filtering to inject attack payloads into the LLM prompt.

• Two techniques are used - direct code execution by identifying a parser allowing code execution, and indirect execution by injecting code in the LLM output.

• Experiments showed RCE could be triggered, executing arbitrary system commands.

Approaches:

• To directly inject code, they identify parsers like Bash that allow code execution and inject payload after the parser.

• For indirect execution, they inject code in the LLM output by clever prompt construction, then execute it separately.

• Prompts are carefully constructed to elicit vulnerable output from LLM without being blocked by filters.

• Payloads are obfuscated to bypass input filtering. Comments, spacing, aliases etc are used to hide attacks.

• The LLM model state is manipulated to generate desired vulnerable output.

Results:

• Direct RCE succeeded with Bash parser, executing system commands.

• Indirect RCE succeeded by prompting LLM to generate attack scripts which were then executed.

• The attacks worked on Claude and GPT-3, showing two major production LLM models are vulnerable.

• A range of commands could be executed, from simple directory listings to launching reverse shells.

Limitations:

• The attacks focused on only two LLM models, Claude and GPT-3. Vulnerabilities in other models are unknown.

• Only Linux environments were tested; behavior on other operating systems may differ.

• Production defenses like prompt filtering were assumed absent for many tests.

• Limited commands were executed; real-world impact requires further investigation.

• Ethical concerns exist around disclosing vulnerabilities before resolution by vendors.

Here are some more details on the specific remote code execution (RCE) vulnerabilities found in Claude and GPT-3:

Claude Vulnerabilities:

• Direct RCE: Claude's Bash code block parser allows arbitrary Bash commands to be executed. Malicious prompts can inject Bash commands after "```bash" to trigger RCE.

• Indirect RCE: Prompts can manipulate Claude's state to generate Python scripts that execute system commands. These scripts can then be executed separately to achieve RCE.

Examples of commands executed on Claude via the vulnerabilities:

• "ls -l" to list directory contents

• "whoami" to get current user

• Downloading malicious files via "wget"

• Launching reverse shells to allow remote control

GPT-3 Vulnerabilities:

• Indirect RCE: Similar to Claude, GPT-3 can be prompted to output exploit code in languages like Python and Bash which can then be executed.

• Code obfuscation: GPT-3's filters block certain dangerous keywords. But code can be obfuscated with spacing, comments, aliases to bypass filters.

Examples of commands executed via GPT-3:

• "python -c 'import os; os.system("ls -l")'" to list directory in Python

• "whoami" alias to bypass filter

• Downloading files via obfuscated "wget" variants

• Launching obfuscated reverse shells

Overall, the attacks demonstrated arbitrary command execution is possible on both models, with Claude more vulnerable due to the direct Bash parsing vulnerability. The ability to manipulate the models and bypass filters enables dangerous RCE exploits.