Role Roadmap * 8 Stages * 4-8 Months to Job-Ready
Your path to becoming an ML Engineer
From Python fundamentals to production ML systems -- a structured, opinionated roadmap built around what top companies actually hire for. No filler, no guesswork.
💰 US Salary Expectations
Junior$85K – $120K
→
Mid-level$120K – $175K
→
Senior$175K – $240K
Full market data →
ML Engineer
Build, train, and scale machine learning systems that power real products
$115k
Avg US Salary
High
Job Demand
6-12mo
Time to Job-ready
Python
Primary Language
Skills You'll Build
✓ Python
✓ Scikit-learn
✓ PyTorch
✓ SQL
+ TensorFlow
+ Docker
+ FastAPI
+ MLflow
+ AWS SageMaker
Kubernetes
Spark
Transformers
Ray
Essential
Strongly Recommended
Nice to Have
Salary Range (UK)
$70-90k
Junior
0-2 years
$95-120k
Mid-level
2-5 years
$125-175k
Senior
5+ years
8 Stages
01
Python & Math Foundations
Build rock-solid Python fluency and the mathematical intuition needed for ML. We cover only what you actually use -- not a maths degree in disguise.
Python data structures & OOP
NumPy & vectorised operations
Pandas for data manipulation
Linear algebra essentials
Statistics & probability basics
Calculus intuition for ML
Resources
Python for Data Analysis -- Wes McKinneyBook
3Blue1Brown -- Essence of Linear AlgebraVideo
NumPy + Pandas Exercises -- KaggleLab
Mini Project: EDA on a real dataset
Perform exploratory data analysis on a public dataset (housing prices or Titanic). Clean, visualise, and summarise key findings in a Jupyter notebook.
Common interview question
What is the time complexity of fitting linear regression with n samples and p features using closed-form solution vs gradient descent?
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02
Core ML Algorithms
The ML algorithms you'll actually use on the job. Understand them intuitively, implement them from scratch, then use Scikit-learn efficiently.
Linear & logistic regression
Decision trees & random forests
Gradient boosting (XGBoost)
K-means & clustering
Model evaluation & metrics
Cross-validation & pipelines
Resources
Hands-On ML -- Aurélien GéronBook
Scikit-learn Official DocsDocs
Kaggle Titanic CompetitionProject
Project: End-to-end ML Pipeline
Build a complete classification pipeline: data ingestion -> feature engineering -> model training -> evaluation. Use Scikit-learn Pipelines and log metrics.
Common interview question
Explain the bias-variance tradeoff. When would you choose logistic regression over XGBoost for a production classification system?
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03
Deep Learning & Neural Networks
PyTorch from scratch. Understand backprop, build CNNs, and train models on real GPU hardware.
Neural network architecture
Backpropagation & optimisers
PyTorch tensors & autograd
CNNs for vision tasks
Regularisation techniques
Transfer learning
Project: Image Classifier with PyTorch
Fine-tune a pretrained ResNet on a custom image dataset. Deploy it as a simple FastAPI endpoint.
Common interview question
Explain backpropagation and the chain rule. What causes vanishing gradients and how do residual connections in ResNets address it?
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04
LLM Integration & Fine-tuning
In 2026, every ML engineer works with LLMs. Master the APIs, learn when to fine-tune vs prompt-engineer, build RAG systems, and evaluate LLM outputs systematically. This stage bridges classical ML with the modern AI-first stack.
Tokenisation & embeddings
Attention & transformers
HuggingFace ecosystem
Fine-tuning BERT / LLaMA
RAG pipeline architecture
Vector databases
Common interview question
When would you fine-tune an LLM vs build a RAG system vs just engineer a better prompt? Walk through your decision process.
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05
MLOps & LLMOps
Production ML requires more than training. Master containerisation, CI/CD for ML models, experiment tracking, and the emerging LLMOps stack: prompt caching, model routing, cost dashboards, and monitoring for both classical and LLM-powered systems.
Docker & containerisation
FastAPI model serving
MLflow tracking
CI/CD with GitHub Actions
AWS SageMaker basics
Model monitoring & drift
Common interview question
Your deployed model precision has dropped 8% over 3 months. How do you diagnose whether it is data drift, concept drift, or a pipeline bug?
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06
System Design for ML
How to design real-time inference systems, feature stores, and ML platforms from scratch.
Common interview question
Design a real-time fraud detection ML system handling 10,000 transactions per second with sub-10ms latency requirements.
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07
Interview Prep & Portfolio
Crack the ML interview: coding rounds, ML theory questions, system design, and how to present your portfolio.
Common interview question
Tell me about a production ML system you built. What monitoring did you put in place and what failure mode surprised you most?
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08
AI-Augmented ML Engineering
The frontier skill separating 2026 ML engineers: using LLMs to accelerate your own ML workflows. Learn to generate synthetic training data, automate feature engineering with AI, explain model predictions in natural language, and build hybrid systems that combine classical ML with LLM reasoning.
LLM-powered feature engineering
Synthetic training data generation
Weak supervision & AI data labelling
AutoML + LLM orchestration
Hybrid ML + LLM architectures
Explaining models in natural language
LLM-as-judge for model evaluation
AI-assisted debugging & root cause analysis
Recommended Resources
Hamel Husain — Synthetic Data Generation Patterns Article
Snorkel AI — Programmatic Weak Supervision Docs
Anthropic — Claude Tool Use & Pipelines Docs
deeplearning.ai — AI Agents in LangGraph Course
Capstone: LLM-Augmented ML Pipeline
Build an end-to-end pipeline where an LLM generates 500 synthetic training examples for a text classifier, evaluates the model with LLM-as-judge, and produces a natural-language explanation of the model failures on 5 hard edge cases. Track all experiments with MLflow. Estimated: 10-14 hours.
Common interview question
You have 500 labelled examples and 50,000 unlabelled ones. How would you use LLMs to expand your training set, and how would you validate that the synthetic data actually improves model performance?
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Career Outcomes
Jobs this roadmap opens
Roles you’ll qualify for after completing all 8 stages
ML Engineer
● Very High Demand
20k–80k US · £65k–£110k UK
Applied AI Engineer
● Very High Demand
30k–90k US · £70k–£120k UK
Data Scientist
● High Demand
00k–55k US · £55k–£90k UK
MLOps Engineer
● Very High Demand
25k–75k US · £65k–£105k UK
AI Research Engineer
● High Demand
40k–20k US · £75k–£130k UK
Full-Stack AI Engineer
● Explosive Growth
30k–00k US · £70k–£120k UK
Career Planning
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