5 AI Engineer Projects to Level Up Your Skills

Forget passive scrolling—real AI engineers build. Here are 5 hands-on AI/ML projects to enhance your skills:

1. Agent Design Patterns

• Implement Reflection, Planning, Tool Use, and MultiAgent Patterns in Python with Groq LLMs—no LangChain or LlamaIndex.
  🔗 https://lnkd.in/dhjpzzeg

2. Rick LLM

• Fine-tune Llama 3.1 using Unsloth AI on Rick & Morty transcripts and deploy via Ollama.
  🔗 https://lnkd.in/dKrYfTA6

3. Twin Celebrity App

• Build an AI-powered celebrity twin finder using Qdrant, FaceNet, ZenML, Streamlit, and Google Cloud Run.
  🔗 https://lnkd.in/denbkwTr

4. Ava, the WhatsApp Agent

• Create a multimodal WhatsApp agent with LangGraph, TTS/STT pipelines, diffusion models, and VLMs.
  🔗 https://lnkd.in/dE4xgZPx

5. PhilioAgents – AI Meets Philosophy

• Simulate historical philosophers in an interactive game using AI agents.
  🔗 https://lnkd.in/d5d2aqQY

You Should Know:

1. Running LLMs Locally (Ollama)

ollama pull llama3 
ollama run llama3 "Explain quantum computing" 

2. Fine-Tuning with Unsloth

from unsloth import FastLanguageModel 
model = FastLanguageModel.from_pretrained("llama3") 
model.finetune(dataset="rick_morty.json") 

3. Deploying Streamlit Apps

streamlit run app.py 
 Deploy to Cloud Run 
gcloud run deploy --source . --platform managed 

4. WhatsApp Bot with Twilio

from twilio.rest import Client 
client = Client(account_sid, auth_token) 
client.messages.create(body="AI Response", from_="whatsapp:+123456", to="whatsapp:+789012") 

5. Multi-Agent Systems

from groq import Groq 
client = Groq(api_key="your_key") 
response = client.chat.completions.create(model="mixtral", messages=[{"role": "user", "content": "Plan a task"}]) 

What Undercode Say:

AI engineering thrives on hands-on experimentation. Whether fine-tuning LLMs, deploying cloud-based AI apps, or building multi-agent systems, practical implementation beats theoretical knowledge. Expect more AI-agent integrations in everyday tools (e.g., WhatsApp, Slack) and increased demand for locally run, privacy-focused models like Ollama.

Prediction:

AI agent frameworks will dominate workflow automation by 2025, with LLM fine-tuning becoming as common as web scraping.

Expected Output:

A structured guide with executable commands and project URLs for aspiring AI engineers.

References:

Reported By: Migueloteropedrido Builders – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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