How to become a Machine Learning Engineer

Ideal starting backgrounds

You're ready to start this path if you can:

• Write production Python (classes, testing, error handling, logging)
• Understand ML fundamentals (supervised vs. unsupervised, overfitting, cross-validation)
• Build and evaluate simple ML models (scikit-learn, basic neural networks)
• Query databases with SQL
• Use Git and understand CI/CD basics

Not ready yet? Start with ML Foundations or Data Engineer path (CP41) first.

Study hours required: ~500–600 hours total. Assumes 12–15 hours/week = 30 months. This path is intense; many combine study with full-time work.

All figures: median base salary, not including bonuses/equity. ZAR = USD × 18. Sources: Robert Half 2026, Levels.fyi, LinkedIn Salary.

South Africa note: ML Engineers are rare in SA; most work remotely for international companies. Entry-level remote roles: R80,000–R120,000/month. Mid-level: R130,000–R180,000/month. Senior: R160,000–R250,000/month. Johannesburg-based roles at banks/FinTech: R70,000–R100,000/month for entry (lower than remote). Remote is strongly recommended for SA ML engineers.

Salary accelerators: AWS + GCP certs, Databricks expertise, Python proficiency, MLOps knowledge, and GPU/deep learning experience all command 15–25% premiums.

Month 0–6: Build the ML Foundation

1. Take Andrew Ng's ML course — Coursera ML course or CS229. This is the reference. 6–8 weeks.
2. Master Python for ML — Fast.ai or Kaggle Learn. Code-first, practical approach.
3. Build 5 ML projects — Use scikit-learn and Pandas. Regression, classification, clustering. Post to GitHub. 30+ hours hands-on.
4. Join communities — r/MachineLearning, MLOps.community Slack, local ML meetups.
5. Contribute to open source — Find ML libraries on GitHub (scikit-learn, XGBoost, Hugging Face). Contribute small features or bug fixes. Builds credibility.

Month 6–12: Build Your ML Engineering Portfolio

• Project 1: End-to-End Classification Pipeline — Build a Titanic/Iris prediction system. Include: data exploration, feature engineering, model training (try 5+ algorithms), hyperparameter tuning, cross-validation, evaluation. Estimated time: 15 hours.
• Project 2: Cloud ML Deployment — Take Project 1, deploy to AWS SageMaker or GCP Vertex AI. Create a REST API. Set up monitoring for predictions. Estimated time: 12 hours.
• Project 3: NLP or Time Series — Pick a domain (sentiment analysis, stock prediction, etc.). Build an end-to-end ML system. Include feature engineering, model training, deployment. Estimated time: 20 hours.

Month 12–24: Pursue Certifications & Apply

• AWS MLS-C01: Prepare 4–6 months. Use Stephane Maarek's course + hands-on labs.
• GCP ML Engineer: Prepare 3–4 months. Use Google Cloud training.
• CV positioning: List as "ML Engineer" once you hold AWS MLS-C01. Highlight Python, cloud platforms, and portfolio projects.
• Target companies: Tech companies, FinTech, InsurTech, E-commerce (Takealot), analytics platforms. Remote/international roles highly available.
• Interview prep: Be ready to discuss 1) Your ML projects end-to-end, 2) Feature engineering approaches, 3) Model evaluation metrics, 4) Hyperparameter tuning, 5) Debugging ML systems, 6) ML ops/monitoring.

ML Engineer at Capitec (Johannesburg) — Junior Level

08:00 — Standup with the ML team. You're working on a credit risk model. Data scientist trained a new XGBoost model; you're responsible for deploying it.

09:00 — Review the model artifact. Check evaluation metrics, feature importance, and potential bias. Metrics look good. Write a deployment plan: test in staging, set up monitoring for prediction latency and distribution drift.

10:00 — Set up a Docker container for model serving. Use Flask to wrap the model. Add logging and error handling. Test locally with sample data.

11:30 — Deploy to AWS SageMaker endpoint. Set up CloudWatch monitoring. Configure alarms for model inference latency and error rates.

13:00 — Lunch.

14:00 — Work on feature engineering. The fraud detection team wants a new feature: "days since account creation." Write Python code to engineer this from raw data. Add it to the feature store.

15:00 — Code review with a senior engineer. Feedback: add more logging, handle edge cases (accounts with missing creation dates), and add unit tests. You revise.

16:00 — Help a data scientist debug a training issue. The model performance dropped after retraining. Investigate: the data distribution changed. Discuss potential causes and retraining strategy.

17:00 — End of day. All systems green. Push code to GitHub. Plan tomorrow: start on GCP ML certification.

ML Engineer at a London AI startup (Remote/Cape Town) — Mid Level

09:00 — Async standup. You're leading the MLOps initiative—setting up CI/CD for ML models, automated retraining, and monitoring.

09:30 — Code review. The data science team trained a new recommendation model. You check the model artifact, evaluation, and training pipeline code. Good, but missing automated retraining triggers. Suggest using MLflow + Airflow.

10:30 — Meet with the data scientist who trained the model. Design the monitoring strategy: model accuracy on holdout test, prediction distribution monitoring, inference latency. Document in a design doc.

11:30 — Implement model monitoring. Use Evidently AI (open source) to track prediction drift. Set up alerts if drift exceeds 5%.

12:30 — Lunch.

13:30 — Set up automated retraining pipeline using Airflow. Model retrains weekly on new data if drift is detected. Include validation: new model must beat production baseline before deployment.

15:00 — Document the entire MLOps architecture. Create a runbook for the team. Include: how to deploy new models, monitor for drift, rollback if needed.

16:00 — Pair programming with a junior ML engineer. They're learning to set up model serving. Review their Docker config, help optimize for latency.

17:00 — Respond to a production incident. A model's inference latency spiked. Investigate: the input feature distribution changed unexpectedly. Increase model endpoint capacity. Post-mortem scheduled for tomorrow.

17:30 — End of day. Start on Databricks ML certification tomorrow.

Market specifics

ML Engineers are a niche but rapidly growing role in South Africa. Nedbank, Standard Bank, and Capitec all have ML teams for fraud detection, credit risk, and customer analytics. Takealot uses ML for recommendations and supply chain optimization. FinTech companies (Luno, PayFast) are hiring ML engineers for risk and compliance.

Most South African ML engineers work remotely for UK/US companies at significantly higher salaries (£60k–£100k+ = R1.08M–R1.8M+/year vs. R840k–R1.2M locally). The skillset is globally valuable, and remote-first job search is strongly recommended.

ML engineering is still less established in SA than data engineering or analytics, so demand is lower but roles are high-paying and growth is fast. Government and large enterprises (Eskom, Transnet) are beginning to invest in ML but move slowly.

SA-specific resources

Q: Do I need a PhD to become an ML Engineer?

No. Most ML engineers have a bachelor's degree (CS, math, physics) or are self-taught. A PhD is not required. Hands-on experience and certs matter more.

Q: Should I start with ML or data engineering?

Either. Data engineer path is broader; ML engineer is specialized. If unsure, start with data engineering (CP41), then pivot to ML. Or start with ML fundamentals (Andrew Ng's course) to test fit.

Q: How long does it really take from zero?

24–36 months if starting with no data/ML background. If you're a software engineer already, 18–24 months. If you're a data scientist, 12–18 months. This is not a fast path—expect significant study and project work.

Q: Is AWS MLS-C01 worth it for this role?

Yes. 70%+ of job postings mention AWS for ML. The MLS-C01 exam is tough but proves you understand SageMaker and cloud ML systems. Essential credential.

Q: What's the difference between ML Engineer and Data Scientist?

Data Scientists focus on model design, experimentation, and research. ML Engineers focus on production—deployment, monitoring, scalability. Both need ML knowledge but apply it differently. ML Engineer = software engineer + ML. Data Scientist = researcher + statistics.

Q: Can I do this path while working full-time?

Yes, but it's challenging. Expect 15–20 hours/week of study + projects. Most people combine working as a software engineer or data engineer with ML study, then transition to ML engineer role.

Template version: 2026-05-02 | Maintained by IT Career Roadmap | ZAR baseline: R18/$1 USD File naming: Career_Paths/CP45_Data_ML_Engineer.md