Career Roadmap

Target Roles

AI Engineer / LLM Engineer (\(140k-\)280k)

Build production AI applications: RAG systems, agents, prompt pipelines, fine-tuned models.

Must-have: RAG, prompt engineering, LLM APIs (OpenAI/Anthropic/local), vector databases, Python, FastAPI, Docker Nice-to-have: LoRA/QLoRA fine-tuning, AI agents, MCP, LangChain/LlamaIndex, MLOps

ML Engineer (\(130k-\)250k)

Build, train, deploy, and maintain ML models at scale.

Must-have: PyTorch, scikit-learn, model training/eval, Docker, CI/CD, cloud (AWS/GCP/Azure), SQL Nice-to-have: Distributed training, MLOps (MLflow, W&B), Spark, LLM serving (vLLM)

Data Scientist (\(110k-\)200k)

Analyze data, build models, run experiments, communicate insights.

Must-have: Python (pandas, NumPy, scikit-learn), statistics, SQL, visualization, communication Nice-to-have: Causal inference, time series, deep learning, GenAI literacy

MLOps / AI Platform Engineer (\(130k-\)230k)

Build infrastructure for ML/AI teams.

Must-have: Docker, Kubernetes, CI/CD, cloud ML platforms, MLflow/W&B, monitoring, IaC (Terraform)

Skills Matrix

Skill

AI Eng

ML Eng

Data Sci

MLOps

Python

Y

Y

Y

Y

SQL

-

Y

Y

-

Statistics

-

Y

Y

-

PyTorch

-

Y

-

-

LLM APIs + RAG

Y

-

-

-

Prompt engineering

Y

-

-

-

Fine-tuning

Y

Y

-

-

AI agents

Y

-

-

-

Docker / CI/CD

Y

Y

-

Y

Cloud (AWS/GCP)

-

Y

-

Y

Kubernetes

-

-

-

Y

90-Day Plan

Days 1-30: Foundation

  • Set up environment, choose learning track

  • Complete Phase 0-1 (foundations, Python/data science basics)

  • First small project on GitHub

Days 31-60: Core Skills

  • AI Engineer: Embeddings, vector DBs, basic RAG → deploy RAG chatbot

  • ML Engineer: Math, neural networks, PyTorch → deploy ML pipeline with MLflow

  • Data Scientist: Statistics, causal inference → EDA + predictive model, write a blog post

Days 61-90: Advanced + Job Prep

  • Complete advanced topics for your track

  • Build second portfolio project

  • Polish portfolio, update resume/LinkedIn, start applying

  • Practice with INTERVIEW_PREP.md

Portfolio (Minimum 3 Projects)

Project

Shows

Tech

RAG Chatbot

You can build AI apps

LangChain, ChromaDB, OpenAI/Ollama, FastAPI

Fine-tuned Model

You understand training

QLoRA, Hugging Face, eval metrics

MLOps Pipeline

You can ship to production

MLflow, Docker, GitHub Actions, FastAPI

What makes a good project README: What it does, architecture diagram, how to run it, key design decisions, results/metrics, what you learned.

Free deployment: HuggingFace Spaces, Render, Railway, Vercel, GitHub Pages

Where to Apply

AI/ML job boards: ai-jobs.net, MLOps.jobs, HuggingFace Jobs, LinkedIn, Wellfound, Levels.fyi

Best for first role: AI-native startups, mid-size tech companies, consulting firms

Strategy: 5-10 applications/week, customize for top choices, follow up after 1 week

Networking

  1. Contribute to open source (even docs/tests on LangChain, HF)

  2. Write blog posts (1 per project, share on LinkedIn/X)

  3. Join communities: HuggingFace Discord, r/MachineLearning, r/LocalLLaMA

  4. Attend local AI/ML meetups

Certifications (Optional)

Certification

Provider

Value

AWS ML Specialty

AWS ($300)

High

Google Professional ML Engineer

GCP ($200)

High

HuggingFace NLP Course

HF (Free)

Medium

Skip generic “AI fundamentals” certificates from non-technical platforms.