ML Engineer
Building, training, and deploying machine learning models to production
ML Engineer is a role in the ML & AI Engineering family. It has 58 skills across 5 levels (from Junior to Principal). 125 skills are mandatory. Key domains: Programming Fundamentals, Backend Development, Database Management.
Technology Stack
Focus by Level
Training models using existing pipelines. Feature engineering. Model validation. Preparing datasets. Working with Jupyter notebooks.
Designing ML pipelines. Model selection and tuning. A/B testing models. Deploying models to production. Feature store.
ML systems architecture. Inference optimization (ONNX, TensorRT). Designing real-time ML. Researching new approaches. Mentoring.
ML platform strategy. MLOps infrastructure. Coordinating ML and backend. Experimentation standards. ML team roadmap.
Company AI strategy. LLM integration. ML at scale. Research agenda. Publications and talks.
Skill Matrix
58 skills × 5 levels. Click on a cell for details.
AI-Assisted Development
4 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| GitHub Copilot | A | A | W | A | A |
| Cursor IDE | A | W | A | A | — |
| ChatGPT / Claude | A | A | W | A | A |
| Prompt Engineering for Code | A | A | W | A | A |
API & Integration
5 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| REST API Design | A | W | A | A | E |
| GraphQL Design | A | W | A | E | E |
| WebSocket API Design | A | W | A | E | E |
| gRPC & Protocol Buffers | A | A | W | A | A |
| API Documentation | A | W | A | E | E |
Architecture & System Design
1 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| System Design Fundamentals | A | W | A | E | E |
Backend Development
4 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Python Web Frameworks | A | W | A | A | E |
| Apache Kafka | A | A | W | A | A |
| Redis | A | A | W | A | A |
| Task Queues | A | A | W | A | A |
Cloud & Infrastructure
5 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Docker | A | W | A | A | E |
| Kubernetes Core | A | W | A | A | E |
| Terraform | A | W | A | E | E |
| AWS | A | W | A | A | E |
| Network Fundamentals | A | W | A | E | E |
Data Engineering
4 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Apache Spark | A | W | A | E | E |
| Pandas / Polars | A | W | A | E | E |
| SQL-based ETL | A | W | A | E | E |
| Data Quality | A | W | A | E | E |
Database Management
4 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| PostgreSQL | A | W | A | A | E |
| Database Indexing | A | A | W | A | A |
| Query Optimization | A | A | W | A | A |
| Data Modeling & Schema Design | A | W | A | E | E |
DevOps & CI/CD
1 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| GitHub Actions / GitLab CI | A | W | A | A | E |
Machine Learning & AI
9 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Classical ML (scikit-learn) | A | W | A | E | E |
| Gradient Boosting | A | W | A | E | E |
| PyTorch | A | W | A | E | E |
| MLflow | A | W | A | E | E |
| Feature Stores | A | W | A | E | E |
| Model Serving | A | W | A | E | E |
| Experiment Tracking | A | W | A | E | E |
| ML Pipelines | A | W | A | E | E |
| Model Monitoring | A | W | A | E | E |
Observability & Monitoring
4 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Structured Logging | W | A | E | — | — |
| Prometheus & Grafana | A | W | A | — | — |
| OpenTelemetry | A | W | A | E | E |
| SLI / SLO / SLA | A | W | A | E | E |
Programming Fundamentals
9 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Algorithms & Complexity | W | W | A | A | E |
| Data Structures | W | W | A | A | E |
| OOP & SOLID Principles | W | W | A | A | E |
| Design Patterns | W | W | A | A | E |
| Multithreading | W | W | A | A | E |
| Async Programming | W | W | A | A | E |
| Code Quality & Refactoring | W | W | A | A | E |
| Type Safety & Type Systems | A | W | A | E | E |
| Memory Management | A | W | A | E | E |
Security
3 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| OWASP & Application Security | A | W | A | E | E |
| Secure Coding Practices | A | W | A | E | E |
| JWT / OAuth2 / OIDC | A | W | A | E | E |
Testing & QA
3 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Unit Testing | A | W | A | A | E |
| Integration Testing | A | W | A | A | E |
| E2E Testing | A | W | A | E | E |
Version Control & Collaboration
2 skills| Skills | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Git Advanced | A | W | A | A | E |
| Code Review | A | W | A | A | E |
FAQ
What skills are needed for the ML Engineer role?
The ML Engineer role requires 58 skills, of which 125 are mandatory. Skills are distributed across 5 levels: from Junior to Principal. See full matrix.
How to advance to the next level in the ML Engineer role?
Use the Grade Calculator to assess your current level and get personalized recommendations. The system will show which skills need to be developed for the next level.
What tech stack is used in the ML Engineer role?
The stack includes 5 technologies at different levels. Python 3.11+, scikit-learn, pandas/numpy, Jupyter, PyTorch/TensorFlow basics, SQL, Git, Python 3.12+, PyTorch/TensorFlow, XGBoost/LightGBM/CatBoost, MLflow, Airflow, Feature store basics, Docker, DVC, PyTorch advanced, ONNX/TensorRT, Triton Inference Server, Kubeflow/MLflow, Spark ML, Ray, Kubernetes, Prometheus/Grafana...
How does the community define requirements for the ML Engineer role?
Role requirements are shaped by the community through a proposal system. Any member can suggest changes that go through voting and expert review.