What Does an ML Engineer Do?
An ML Engineer bridges research and production. They take raw data and deliver reliable, scalable machine learning systems that run in production.
Typical responsibilities:
- Design and implement ML training pipelines
- Feature engineering and data preprocessing at scale
- Model training, evaluation, and iteration
- A/B testing ML models in production
- MLOps: versioning, serving, monitoring model performance
- Collaborate with data scientists and software engineers
Who hires ML Engineers: large tech companies, ML-first startups, financial services, healthcare, recommendation systems teams.
Skills Required
Must-Have
- Python — scikit-learn, pandas, NumPy fluency
- Machine learning fundamentals — supervised/unsupervised learning, loss functions, optimization
- Statistics — probability, distributions, hypothesis testing
- Model evaluation — cross-validation, metrics, bias-variance tradeoff
- Feature engineering — transforming raw data into ML-ready features
- MLOps basics — experiment tracking, model versioning, pipeline management
Important
- Deep learning — PyTorch or TensorFlow, neural network architectures
- SQL and data warehousing — accessing training data at scale
- Cloud ML platforms — SageMaker, Vertex AI, or Azure ML
- Distributed training — multi-GPU, data parallelism basics
Nice to Have
- Spark/Dask — large-scale data processing
- Kubernetes and Docker — containerized model serving
- Recommendation systems — collaborative filtering, matrix factorization
- LLM integration — incorporating foundation models into ML pipelines
Learning Path
Phase 1: Python, Math & Statistics Foundations (Weeks 1–6)
Learn:
- Python for AI Complete Guide — scientific Python stack
- Linear Algebra for AI — vectors, matrices, dot products
- Statistics for Machine Learning — probability, distributions, evaluation
Practice:
- Complete exercises with NumPy and pandas on real datasets
- Kaggle: Getting Started competitions (Titanic, House Prices)
Milestone: You understand why algorithms work, not just how to call them.
Phase 2: Machine Learning Fundamentals (Weeks 7–12)
Learn:
- Machine Learning Basics for Developers — core algorithms
- Supervised Learning Guide — regression, classification, ensembles
- Feature Engineering Guide — transforming data for better models
- Model Evaluation and Metrics — measuring what matters
- ML Project Workflow — end-to-end project lifecycle
Build:
- Sentiment Analyzer — text classification
- Complete a Kaggle tabular competition end-to-end
Milestone: You can take a raw dataset from EDA to a deployed scikit-learn model.
Phase 3: Deep Learning (Weeks 13–18)
Learn:
- Neural Networks from Scratch — build to understand
- Deep Learning Fundamentals — CNNs, RNNs, transformers
- PyTorch for AI Developers — hands-on framework
Build:
- Build a custom image classifier with PyTorch + ResNet transfer learning
- Fine-tune BERT on a text classification task
Milestone: You can train, evaluate, and export a PyTorch model.
Phase 4: MLOps & Production (Weeks 19–24)
Learn:
- Deploying AI Applications — serving models in production
- AI Application Architecture — system design patterns
Build:
- Set up MLflow for experiment tracking on your ML projects
- Containerize a model with Docker and serve it with FastAPI
- AI Data Analyst — LLM + pandas integration
Milestone: You have a complete ML project with tracking, versioning, and a served API.
Phase 5: LLMs for ML Engineers (Weeks 25–28)
Learn:
- How LLMs Work — pretraining, RLHF, inference
- Fine-Tuning LLMs Guide — LoRA, QLoRA, instruction tuning
- LLM Inference and Serving — production serving
Build:
- AI Code Review Assistant — combines ML + LLM patterns
Milestone: You can fine-tune an open-source LLM and serve it for inference.
Recommended Projects (In Order)
| Project | Skills | Level |
|---|---|---|
| Sentiment Analyzer | Classification, pandas | Beginner |
| AI Quiz Generator | JSON mode, structured output | Beginner |
| AI Data Analyst | pandas + LLM code gen | Intermediate |
| AI Code Review Assistant | Diff parsing, GitHub API | Advanced |
| AI Security Analyzer | Static analysis, SAST | Advanced |
Key Tools to Know
| Category | Tools |
|---|---|
| Experiments | MLflow, Weights & Biases |
| Data | pandas, DVC, Great Expectations |
| Training | PyTorch, scikit-learn, XGBoost |
| Serving | FastAPI, TorchServe, Triton |
| Orchestration | Airflow, Prefect |
| Cloud | SageMaker, Vertex AI |
Interview Topics
- Explain the bias-variance tradeoff and how you handle it
- How do you handle class imbalance in a classification problem?
- What metrics would you use for a fraud detection model?
- Describe your MLOps workflow for a production model
- What's the difference between batch and online learning?
- How do you detect and handle data drift?
Next Paths to Explore
- AI Research Engineer Path — go deeper on theory and novel methods
- AI Engineer Path — pivot to building LLM-powered applications