100DaysOfAIEngineer

💼 Job Hunting Playbook - Days 101-130 & Beyond

You completed 100 days. You’re an AI Engineer. Now let’s get you HIRED.

🎯 The Goal: AI Engineering Role in 30-90 Days

This playbook covers:

Timeline: 30 days of focused job hunting

📅 Days 101-130: Job Hunting Sprint

Week 1 (Days 101-107): Foundation

Week 2 (Days 108-114): Applications Begin

Week 3 (Days 115-121): Interview Prep Intensifies

Week 4 (Days 122-130): Full Court Press

📝 Step 1: Resume Transformation

Your Resume Should SCREAM AI Engineer

❌ Before 100 Days:


Skills: Python, ML, Data Analysis
Projects: None

✅ After 100 Days:


AI ENGINEER
Specialized in Computer Vision, NLP, and Production ML Systems

SKILLS:
• Deep Learning: PyTorch, CNNs, Transformers, Transfer Learning
• Computer Vision: Object Detection (YOLO), Segmentation, GANs
• NLP/LLMs: BERT, GPT, RAG, LangChain, Prompt Engineering
• MLOps: Docker, MLflow, CI/CD, FastAPI, Cloud Deployment
• Tools: NumPy, Pandas, scikit-learn, Hugging Face, Vector DBs

PROJECTS:
[7 impressive projects with metrics and links]

Resume Structure:


[YOUR NAME]
AI Engineer | Machine Learning | Computer Vision | NLP
[Email] | [Phone] | [LinkedIn] | [GitHub] | [Portfolio]

SUMMARY
AI Engineer with expertise in building production ML systems. Completed intensive 100-day curriculum building 7 full-stack AI applications. Specialized in computer vision, NLP, and MLOps. Passionate about deploying AI solutions that solve real problems.

SKILLS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Machine Learning: Regression, Classification, Clustering, Neural Networks, Deep Learning
Computer Vision: CNNs, Object Detection (YOLO), Segmentation, Transfer Learning, GANs
Natural Language Processing: Transformers, BERT, GPT, Fine-tuning, RAG, LangChain
MLOps & Deployment: Docker, MLflow, CI/CD, FastAPI, Model Monitoring, Cloud (AWS/GCP)
Programming: Python, PyTorch, TensorFlow, scikit-learn, NumPy, Pandas
Tools: Jupyter, Git, Hugging Face, Vector Databases (ChromaDB, Pinecone), Streamlit

PROJECTS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

1. PRODUCTION RAG APPLICATION | [GitHub] | [Live Demo]
   • Built end-to-end Retrieval-Augmented Generation system using LangChain and ChromaDB
   • Implemented vector search with 95% retrieval accuracy
   • Deployed with FastAPI backend, handling 100+ concurrent users
   • Tech: Python, LangChain, OpenAI API, ChromaDB, FastAPI, Docker

2. REAL-TIME SURVEILLANCE SYSTEM | [GitHub] | [Demo Video]
   • Developed real-time object detection and tracking system achieving 20 FPS
   • Implemented YOLO v8 with custom alert system for security monitoring
   • Optimized for edge deployment with 40% model size reduction
   • Tech: PyTorch, YOLO, OpenCV, Python, Model Optimization

3. NLP MULTI-TASK API | [GitHub] | [API Docs]
   • Built production API serving sentiment analysis, NER, and text classification
   • Fine-tuned BERT model achieving 92% accuracy on custom dataset
   • Implemented caching and rate limiting for 1000+ requests/hour
   • Tech: Transformers, BERT, FastAPI, Docker, PostgreSQL

4. IMAGE CLASSIFIER WEB APPLICATION | [GitHub] | [Live Demo]
   • Developed CNN-based image classifier with 93% accuracy using transfer learning
   • Built web interface with real-time inference and visualization
   • Deployed on AWS with CI/CD pipeline
   • Tech: PyTorch, ResNet, Flask, AWS EC2, GitHub Actions

5. MLOPS CI/CD SYSTEM | [GitHub]
   • Implemented full MLOps pipeline with automated training, testing, and deployment
   • Set up model monitoring dashboard tracking drift and performance
   • Reduced deployment time from hours to minutes
   • Tech: MLflow, Docker, Jenkins, Prometheus, Grafana

6. ML PIPELINE FOR CLASSIFICATION | [GitHub]
   • Built end-to-end pipeline achieving 0.92 ROC-AUC on imbalanced dataset
   • Implemented feature engineering, hyperparameter tuning, and cross-validation
   • Created automated data preprocessing and model selection framework
   • Tech: scikit-learn, Pandas, NumPy, XGBoost, GridSearchCV

7. CAPSTONE: [YOUR CAPSTONE PROJECT NAME] | [GitHub] | [Demo]
   • [Brief description highlighting complexity and impact]
   • [Key technical achievement with metric]
   • [Deployment/scale information]
   • Tech: [Your tech stack]

EDUCATION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[Your degree] | [University] | [Year]
100 Days of AI Engineer Intensive Program | 2025

CERTIFICATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
• [Any relevant certs - AWS, Google, Deep Learning Specialization]

Resume Tips:

DO:

✅ Lead with metrics (accuracy, FPS, ROC-AUC, users served) ✅ Include GitHub and live demo links ✅ Use technical keywords (ATS optimization) ✅ Show end-to-end project ownership ✅ Quantify impact wherever possible

DON’T:

❌ List technologies you can’t explain ❌ Use vague descriptions (“built ML model”) ❌ Skip GitHub links ❌ Make it longer than 2 pages ❌ Use buzzwords without substance

🌐 Step 2: Portfolio Website

Must-Have Sections:

1. Hero Section


Hi, I'm [Your Name]
AI Engineer specializing in [Your Focus]

[Professional photo]
[GitHub] [LinkedIn] [Email] [Resume]

2. Projects Showcase For each of your 7 projects:

3. Skills Section Organized by category:

4. About Me

5. Blog (Optional but recommended)

6. Contact

Portfolio Platforms:

Easy (No coding):

Developer-friendly:

Showcase platforms:

Pick ONE and build it in Days 101-103. Don’t overthink it.

💼 Step 3: LinkedIn Optimization

Your LinkedIn Should Be a Job Magnet

Profile Headline (NOT just “AI Engineer”):


Examples:

✅ AI Engineer | Computer Vision & NLP | Building Production ML Systems | PyTorch | LLMs
✅ Machine Learning Engineer | 7 Deployed AI Projects | Specialized in RAG & MLOps
✅ AI/ML Engineer | Computer Vision, NLP, MLOps | Open to Opportunities

About Section Template:


AI Engineer passionate about [your focus area].

I recently completed an intensive 100-day AI Engineering program, building 7 production-ready applications spanning:
• Computer Vision (object detection, segmentation)
• Natural Language Processing (transformers, LLMs, RAG)
• MLOps (CI/CD, monitoring, deployment)

WHAT I BUILD:
→ Real-time computer vision systems
→ NLP applications with LLMs
→ Production ML pipelines
→ End-to-end AI solutions

TECH STACK:
Python | PyTorch | Transformers | LangChain | Docker | FastAPI | AWS | MLflow

CURRENTLY:
→ Seeking AI/ML Engineer roles
→ Building [current project]
→ Open to interesting conversations

📫 Let's connect if you're working on AI/ML!

🔗 Portfolio: [link]
💻 GitHub: [link]

Featured Section:

Experience Section: Even if you don’t have professional experience:


AI Engineer | Independent Projects
Jan 2025 - Present

• Completed intensive 100-day AI Engineering curriculum
• Built 7 production-ready AI applications from scratch
• Specialized in computer vision, NLP, and MLOps
• [List your projects with metrics here]

Skills Section: Endorse yourself for:

Ask connections to endorse you.

LinkedIn Activity Strategy:

Post 3-5x per week:

Monday: Project showcase


Just deployed a real-time object detection system! 🚀

Key achievements:
✓ 20 FPS on edge devices
✓ Custom YOLO implementation
✓ Alert system with 99% accuracy

Tech: PyTorch, YOLO, OpenCV

What's your favorite computer vision challenge?

#ComputerVision #MachineLearning #AI
[Add project screenshot/video]

Wednesday: Learning/insight share


Learned a valuable lesson building RAG systems:

Retrieval quality matters MORE than model size.

A well-tuned vector search with a smaller LLM outperforms a large LLM with poor retrieval.

Focus on:
✓ Embedding quality
✓ Chunk strategy
✓ Re-ranking
✓ Context window usage

What's your RAG optimization tip?

#LLMs #RAG #AIEngineering

Friday: Engagement / question


Question for ML Engineers:

What's your go-to metric for imbalanced classification problems?

I've been using ROC-AUC and PR curves, but curious what others prefer and why.

Drop your approach below! 👇

#MachineLearning #DataScience

Engage daily:

📨 Step 4: Application Strategy

Target Companies:

Tier 1: AI-First Companies

Tier 2: Tech Companies with Strong AI Teams

Tier 3: ML/AI Startups

Tier 4: Traditional Companies Building AI

Where to Apply:

Job Boards:

Company Websites:

Networking:

Application Volume:

Week 1: 10-15 applications Week 2: 15-20 applications Week 3: 20+ applications Week 4: 20+ applications

Total: 65-75+ applications in 30 days

Quality > Quantity, but you need volume for statistical success.

Application Tracking Spreadsheet:


| Company | Role | Date Applied | Status | Follow-up Date | Notes |
|---------|------|--------------|--------|----------------|-------|
| OpenAI  | MLE  | 2025-02-01   | Applied| 2025-02-08     | Referral from X |

Track everything. Follow up after 1 week.

🗣️ Step 5: Networking

Networking = Faster Job Offers

The Formula:


Networking → Warm Intro → Interview → Offer
vs.
Cold Application → Rejected (90% of time)

Networking Strategies:

1. LinkedIn Outreach

Message Template (to AI Engineers):


Hi [Name],

I came across your work on [specific project/post] and was impressed by [specific detail].

I recently completed an intensive AI Engineering program, building production systems in computer vision, NLP, and MLOps. [Your portfolio link]

I'm currently exploring opportunities in [area] and would love to learn about your experience at [Company].

Would you be open to a brief chat? Happy to work around your schedule.

Best,
[Your Name]

2. Twitter/X Outreach

3. Discord/Community Networking

4. Coffee Chats

Networking Goals:

Week 1-2: 10 new connections per week Week 3-4: 15 new connections per week

Focus on: AI Engineers, ML Engineers, Hiring Managers, Recruiters

🎯 Step 6: Interview Preparation

Types of Interviews:

1. Recruiter Screen (30 min)

2. Technical Screen (45-60 min)

3. Project Deep-Dive (60 min)

4. ML System Design (60 min)

5. Behavioral (45 min)

Preparation Strategy:

Weeks 1-2: Fundamentals Review

Weeks 3-4: Interview Practice

Common Interview Questions:

ML Fundamentals:


• Explain backpropagation
• Bias-variance tradeoff
• Overfitting vs underfitting
• When to use which algorithm?
• How to handle imbalanced data?
• Explain regularization
• Cross-validation strategies

Deep Learning:


• CNN vs RNN vs Transformer?
• How does attention work?
• Transfer learning strategies
• Batch normalization vs Layer normalization
• How to debug neural networks?

NLP/LLMs:


• How do transformers work?
• BERT vs GPT architecture
• What is RAG?
• Fine-tuning strategies
• Prompt engineering best practices

MLOps:


• How to deploy ML models?
• Model monitoring strategies
• CI/CD for ML
• A/B testing for models
• Model versioning

Project Questions:


• Walk me through [your project]
• Why did you choose [approach]?
• What challenges did you face?
• How would you scale this?
• What would you do differently?

Practice Resources:

Coding:

ML Theory:

System Design:

Mock Interviews:

💰 Step 7: Salary Negotiation

Know Your Worth:

AI/ML Engineer Salaries (US, 2025):

Entry-Level (0-2 years):

Mid-Level (2-5 years):

Your target after 100 days: $90k - $130k depending on location and company size

(Adjust for your country/region)

Negotiation Strategy:

1. Always Negotiate

2. Delay Salary Discussion

3. When You Must Give a Number:

Example:


"Based on my research and the value I'd bring with my expertise in [areas], I'm targeting $110k-$130k, but I'm flexible based on the full package including equity, benefits, and growth opportunities."

4. Negotiate Beyond Salary:

5. Multiple Offers = Leverage

📧 Follow-Up Strategies

After Application:

Template:


Subject: Following up on AI Engineer Application

Hi [Recruiter Name],

I applied for the [Role] position on [Date] and wanted to follow up.

I'm excited about [Company]'s work in [specific area]. My recent projects in [relevant area] align well with [something from job description].

Portfolio: [link]
GitHub: [link]

Would love to discuss how I can contribute to the team.

Best,
[Your Name]

After Interview:

Template:


Subject: Thank you - [Role] Interview

Hi [Interviewer Name],

Thank you for taking the time to speak with me today about the [Role] position.

I particularly enjoyed discussing [specific topic from interview]. It reinforced my excitement about [specific aspect of role/company].

The challenges you mentioned around [problem] align perfectly with my experience building [relevant project].

Looking forward to next steps!

Best,
[Your Name]

📊 Metrics to Track

Application Metrics:


Applications Sent: ___
Responses Received: ___ (Response Rate: __%)
Phone Screens: ___ (Phone Screen Rate: __%)
Technical Interviews: ___ (Interview Rate: __%)
Offers: ___ (Offer Rate: __%)

Typical Funnel:

If your funnel is worse, fix:

🎯 30-Day Checklist

Week 1 (Days 101-107):

Week 2 (Days 108-114):

Week 3 (Days 115-121):

Week 4 (Days 122-130):

🔥 The Bottom Line

You have the skills. Now get the job.

The job hunt is a numbers game + skill:

Most people quit after 10 rejections. Don’t be most people.

Your AI Engineering job is out there. Go get it. 💪🚀

💬 Share Your Wins

Post in Discord when you:

We celebrate every win. The community wants to see you succeed. 🎉

🔗 Related Resources

Days 1-100: You became an AI Engineer.

Days 101-130: You become an EMPLOYED AI Engineer.

Let’s fucking go. 🔥🚀💼