Plan for 6 months

By: Risheek kumar B

🧭 FAANG MLE Job Tracker

🎯 Key Success Metrics

Track your progress:

• ✅ 3 production-ready projects deployed
• ✅ 10+ ML system design problems solved (with diagrams)
• ✅ 100+ LeetCode problems (medium/hard)
• ✅ 5+ informational interviews with FAANG MLEs
• ✅ 2+ open-source contributions
• ✅ 2+ technical blog posts with 1000+ views

🧱 Level 1 – Foundation: Deep Learning Expertise (NLP Focus)

Goal 1: Learn, Implement & Share

1. Complete fast.ai (Part 1 + 2)
2. Implement 3 research papers (NLP-focused)
   • Each with:
     • 📂 GitHub repo (clean, modular, testable code)
     • 📝 Blog write-up (LinkedIn + personal site)
     • 🐦 Short Twitter thread (Document learning)
3. Start LeetCode grind (Round 1)
4. Build your FastHTML homepage (About, Projects, Resume, Blog)
5. Begin visibility loop (LinkedIn + GitHub consistency)

Deliverables:

• ✅ 3 paper implementations
• ✅ Portfolio + GitHub + LinkedIn presence
• ✅ 1 blog post with 1,000+ views

⚙️ Level 2 – Productionization: MLOps & Engineering Depth

Goal 2: Deep Learning MLOps Projects

1. Build 2 deep learning projects with full pipelines:
   • Project 1: Real-time NLP serving system (FastAPI + Docker + basic monitoring)
   • Project 2: End-to-end NLP pipeline (data processing → training → deployment)
2. Learn & apply:
   • 🧩 Docker, CI/CD basics
   • 📈 Monitoring (MLflow / W&B)
   • 🧠 ML System Design (start 1 per week)
3. Continue LeetCode grind (Round 2)
4. Post project breakdowns and diagrams online

Deliverables:

• ✅ 2 deployed projects (with APIs + monitoring)
• ✅ 6 ML system design problems documented
• ✅ 1 open-source infra-level PR (logging/testing/configs)

🚀 Level 3 – Scaling & FAANG-Readiness

Goal 3: Scale, Optimize & Network

1. Scale your deployed projects
   • Add distributed training/inference using Spark/Ray
   • Implement model optimization (quantization, pruning, distillation)
2. Polish engineering rigor
   • Clean, modular, testable code
   • Add proper CI/CD, data validation, and versioning
3. System Design Prep
   • 10+ ML + normal system design problems
   • 2+ mock interviews
4. Networking & Outreach
   • 5+ informational interviews (track in Notion)
   • Leverage for referrals
5. Open Source
   • 2+ contributions
   • Documentation and visibility

Deliverables:

• ✅ All projects scaled + optimized
• ✅ Resume + portfolio finalized
• ✅ Applications out + mock interviews done

🧩 Missing / Weak Pieces (to fix)

1. Resume + Portfolio Optimization

• Build personal portfolio with sections:
  • About | Projects | Blog | Resume | Tech Stack
• GitHub cleanup (pin top 3 projects)

2. Product Depth

• 1 project wrapping an ML model as an API + frontend
• 1 project with monitoring & retraining pipeline

3. ML System Design Early Start

• Start from Goal 2 (not Goal 3)
• 1 problem/week, visualize trade-offs

4. Networking & Interview Practice

• Track informational interviews (Notion)
• 1 mock interview/month (Pramp / Interviewing.io)

5. Software Engineering Rigour

• Focus on:
  • Modular, testable Python
  • Logging, CI/CD, Docker
  • Config management

6. Learning Loop

• Every month:
  • 📄 1 Research paper
  • 🧠 1 Engineering/system blog
  • 👣 1 Career story (MLE journey)
• Post summaries weekly (LinkedIn/Twitter)

7. Advanced Topics (FAANG Differentiators)

• Distributed Computing: Spark / Ray / Data parallelism
• Model Optimization: ONNX, TensorRT, latency-accuracy tradeoffs
• A/B Testing: Experimentation, metrics design, causality
• Cross-functional ML: Translating business needs → ML systems

🗓️ 6-Month Timeline

Month 1 – Foundation + Visibility

• [ ] Setup FastHTML portfolio site
• [ ] Polish GitHub (README, pinned repos)
• [ ] Start fast.ai course
• [ ] Begin first paper implementation
• [ ] 15 LeetCode problems
• [ ] Read 2 ML system design case studies
• Deliverable: Portfolio live + visibility started

Month 2 – Deep Learning + Interview Prep

• [ ] Finish fast.ai
• [ ] Complete first paper implementation + blog
• [ ] Start second implementation
• [ ] 20 LeetCode (mediums)
• [ ] 2 ML system design problems (documented)
• [ ] 2 informational interviews
• Deliverable: 1 paper implementation live + blog

Month 3 – Production ML + Engineering Rigor

• [ ] Third paper implementation
• [ ] Start Project 1: Real-time NLP serving system
• [ ] Learn Docker, FastAPI, CI/CD basics
• [ ] 20 LeetCode problems
• [ ] 2 ML system design problems
• Deliverable: 3 paper implementations + first project draft

Month 4 – MLOps Depth + Monitoring

• [ ] Finish Project 1 with monitoring
• [ ] Start Project 2: Full NLP pipeline
• [ ] 20 LeetCode problems (harder set)
• [ ] 3 system design problems
• [ ] 2 more informational interviews
• Deliverable: Project 1 deployed + monitored

Month 5 – Distributed Systems + Open Source

• [ ] Add Spark/Ray to Project 2
• [ ] 1st open-source contribution
• [ ] 15 LeetCode problems
• [ ] 3 system design problems
• [ ] Learn A/B testing fundamentals
• Deliverable: Project 2 live + 1 open-source PR merged

Month 6 – Polish + Interview Blitz

• [ ] Optimize models (quantization/distillation)
• [ ] 2nd open-source contribution
• [ ] Resume polish
• [ ] 10 LeetCode (review weak spots)
• [ ] 2 mock interviews
• [ ] Final networking push
• Deliverable: All projects polished + applications sent

🧠 Continuous Learning Loop (Monthly)

🧰 Tech Stack Focus

🗃️ Tracking Dashboard (Notion / Spreadsheet)