Playbooks

Practical playbooks for the parts nobody teaches — what to learn, how to follow up after interviews, and how to thrive once you're in.

An AI career playbook for the parts nobody teaches: this page packages three of them. The tech-stack playbook helps you choose what to learn and build with, the interview follow-up playbook covers the days between interview and decision, and the workplace navigation playbook handles thriving once you are actually in.

Each playbook is structured, specific and short enough to act on — the follow-up playbook, for example, covers thank-you notes, timelines and how to nudge without hurting your odds. Use them alongside the career diagnosis for direction, the job-search toolkit for execution and the growth page for what comes next.

Explore: Career diagnosis · Career growth · Job search toolkit

Metadata

Title: AI Tech Stack Choices for Career Growth
Last Updated: 2026-07-15T20:23:08.013673

Framework Choices

Llm Frameworks

Raw Apis

Cons

  • ›More code to write
  • ›Reinvent wheels
Note: OpenAI API, Anthropic API directly

Pros

  • ›Full control
  • ›No abstraction overhead
  • ›Always works
Career Impact: Good to know fundamentals
Recommendation: Understand fundamentals, use frameworks in practice

Langchain

Cons

  • ›Abstractions can be leaky
  • ›Moving fast (breaking changes)

Pros

  • ›Industry standard for LLM apps
  • ›Huge ecosystem
  • ›Most job postings mention it
Trend: Very hot
Market Share: 60% (2026)
Career Impact: ESSENTIAL for LLM roles
Recommendation: MUST LEARN for LLM engineers

Llamaindex

Cons

  • ›Smaller than LangChain
  • ›Fewer job postings

Pros

  • ›Better for RAG
  • ›Clean abstractions
  • ›Good documentation
Trend: Growing
Market Share: 30% (2026)
Career Impact: Important for RAG-heavy roles
Recommendation: Learn after LangChain

Deep Learning Framework

Jax

Cons

  • ›Smaller ecosystem
  • ›Fewer jobs require it
  • ›Steeper learning curve

Pros

  • ›Very fast
  • ›Functional programming style
  • ›Used by some research labs
Trend: Growing in research

Best For

  • ›Research roles
  • ›Google DeepMind
Market Share: 5% (2026)
Career Impact: Niche - only if targeting specific roles
Recommendation: Optional, learn if interested in research

Pytorch

Cons

  • ›Deployment can be tricky
  • ›Less mature production tooling than TensorFlow

Pros

  • ›Industry standard for research and production
  • ›Most job postings require it
  • ›Great community and resources
  • ›Pythonic and flexible
Trend: Growing

Best For

  • ›Research
  • ›Most ML engineering roles
  • ›LLM work
Market Share: 70% (2026)
Career Impact: Essential - learn this first
Recommendation: MUST LEARN

Tensorflow

Cons

  • ›Less intuitive than PyTorch
  • ›Losing mindshare
  • ›Fewer new projects

Pros

  • ›Still used at Google and some companies
  • ›Good production tooling (TF Serving)
  • ›TensorFlow Lite for mobile
Trend: Declining slightly

Best For

  • ›Google roles
  • ›Mobile ML
  • ›Legacy projects
Market Share: 25% (2026)
Career Impact: Nice to know, not essential
Recommendation: Learn after PyTorch if needed

Learning Strategy

Beginner Path

  • ›Month 1-2: Python + basics
  • ›Month 3-4: PyTorch + ML fundamentals
  • ›Month 5: SQL + Git
  • ›Month 6: LLM APIs + LangChain
  • ›Month 7-8: Build 3 projects using above
  • ›Month 9+: Add Tier 2 skills based on target roles

Avoid Shiny Object Syndrome

  • ›Don't learn every new framework that comes out
  • ›Master fundamentals deeply first
  • ›Learn new tools when you need them for a project
  • ›Frameworks change, fundamentals stay
  • ›YAGNI (You Aren't Gonna Need It) applies to skills too

Skill Prioritization

Tier 1 Must Have

Skills

  • ›Python
  • ›PyTorch (or TensorFlow)
  • ›Pandas, NumPy
  • ›SQL
  • ›Git
  • ›Jupyter notebooks
  • ›LLM APIs (OpenAI, Anthropic) for 2026
Description: Required for 80%+ of ML/AI jobs
Time Investment: 3-6 months to proficiency

Tier 4 Specialized

Skills

  • ›C++/Rust (for performance)
  • ›CUDA (for GPU programming)
  • ›TensorRT (for optimization)
  • ›Specific domain tools
Description: Only if role requires
Time Investment: Varies widely

Tier 3 Nice To Have

Skills

  • ›Kubernetes
  • ›Spark
  • ›Airflow
  • ›Ray
  • ›MLflow/W&B
  • ›React (for demos)
Description: Differentiators but not required
Time Investment: 1-3 months each

Tier 2 Highly Valuable

Skills

  • ›Docker
  • ›LangChain (for LLM roles)
  • ›Cloud (AWS/GCP/Azure basics)
  • ›FastAPI (for APIs)
  • ›Prompt engineering
  • ›RAG (for LLM roles)
Description: Opens many doors, worth learning
Time Investment: 2-4 months to proficiency