How to become a MLOps Engineer (Machine Learning Operations)

Ideal starting backgrounds

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

• Explain what a machine learning pipeline is and its stages
• Write Python code to train a simple ML model using scikit-learn or TensorFlow
• Understand DevOps concepts: CI/CD, containerisation, infrastructure-as-code
• Explain model drift and how to detect it
• Understand basic Kubernetes and Docker

Not ready yet? If you're a data scientist without DevOps knowledge, start with DevOps fundamentals. If you're a DevOps engineer without ML knowledge, start with ML fundamentals (3–6 months each).

Study hours required: ~900–1,200 hours total (Stage 1–3). Assumes 20–25 hours/week = 36–60 weeks.

All figures: median base salary, not including bonuses/equity. ZAR = USD × 18 baseline (verified May 2026). Sources: Robert Half 2026, Glassdoor, LinkedIn Salary.

South Africa note: Entry-level MLOps engineers at Johannesburg-based companies earn R64,000–R95,000/month. Mid-level (3–5 years) command R110,000–R170,000/month. Remote work for international tech (Google, Amazon, Meta) yields R150,000–R250,000/month—MLOps salaries are among the highest in tech. Startups using ML aggressively pay R90k–R140k/month.

Salary accelerators: AWS ML Specialty + Databricks certifications command 15–25% premium. Published ML infrastructure designs, open-source MLOps tools, and Kaggle competition wins boost credibility. Advanced expertise in feature stores, model monitoring, and automation drive highest salaries.

Month 0–6: Build the Foundation

1. Start ML fundamentals — Coursera ML by Andrew Ng (free audit). Build 5–6 ML projects on Kaggle. 100+ hours.
2. Learn DevOps basics — If you're not from DevOps: Docker, Kubernetes basics, CI/CD concepts.
3. Combine: Build an ML project with deployment — Use Docker + simple CI/CD for ML model.
4. Join ML/MLOps community — Reddit: r/MachineLearning, r/MLOps. Discord: MLOps communities. Kaggle for competitions.

Month 6–20: Build Your Portfolio

1. Project 1: Complete ML Pipeline in SageMaker or Databricks (14–16 hours) — End-to-end: data ingestion → feature engineering → training (with hyperparameter tuning) → validation → deployment → monitoring. Document all steps. Include versioning and retraining triggers.

2. Project 2: Feature Store Implementation (10–12 hours) — Build a feature store using Feast or Databricks Feature Store. Demonstrate: feature versioning, feature serving, and feature reuse across models.

3. Project 3: Model Monitoring & Retraining (10–12 hours) — Implement monitoring for model performance (not just system metrics). Detect model drift. Trigger automated retraining. Document the monitoring strategy.

4. Project 4: A/B Testing Framework (8–10 hours) — Design and implement A/B testing for ML models. Show: traffic splitting, metrics collection, and statistical significance testing. Document the framework.

Month 20–28: Apply and Iterate

• CV positioning: List yourself as "MLOps Engineer" once you have AWS ML Specialty + Databricks cert + portfolio. Before certs, list as "ML Engineer (MLOps Focus)" or "Data Engineer (ML Ops Track)".
• Target companies: Start with startups using ML aggressively (fintech for fraud detection, e-commerce for recommendations). Then tech companies. Avoid companies without mature ML practices—they won't value MLOps expertise.
• Interview prep: Be ready to discuss: 1) Your end-to-end ML pipeline and architecture decisions; 2) Feature stores and feature engineering at scale; 3) Model monitoring and drift detection; 4) Deployment strategies (canary, blue-green, shadow); 5) A/B testing framework; 6) A production ML incident and how you'd prevent it; 7) Cost optimisation for training/inference.
• Salary negotiation: MLOps roles in SA advertise at R64k–R85k/month entry-level (if they understand MLOps; many list as "data engineer" or "DevOps"). With AWS ML + Databricks, negotiate for R100k–R140k/month. International remote roles are R150k–R250k/month—actively target those. MLOps is one of the highest-paid specialisations.

MLOps Engineer at a Fintech (Johannesburg) — Junior Level

08:00 — Check ML pipeline status. Overnight, a weekly retraining job ran on fraud detection model. Check: model accuracy (99.2%, good), feature quality (all within expected ranges), and training time (2.1 hours, acceptable). Model is ready for deployment to production.

09:00 — Standup with ML team. Brief on overnight retraining and model performance. Data scientists have requested a new feature (transaction amount category). You'll add it to the feature store.

10:00 — Implement the new feature in the feature store. Define: computation logic, versioning, and validation. Test with historical data to ensure correctness.

11:00 — Monitor production model serving. Check: inference latency (45ms average, good), throughput (5,000 predictions/hour, expected), error rate (0.1%, acceptable). All healthy.

12:00 — Lunch.

13:00 — Work on your assigned task: improve model retraining efficiency. Currently, retraining from scratch takes 2.1 hours. Investigate: can you use transfer learning or checkpoint loading to speed up? Design an optimisation.

15:00 — Test the optimisation in staging. New retraining time: 47 minutes (77% faster). Excellent. Document the improvement.

15:30 — Update the retraining pipeline to use the optimisation. Deploy to production pipeline (with manual approval before first run).

16:30 — Document the feature store schema and model monitoring metrics for the team.

17:00 — Wrap up. Check all systems healthy. Plan tomorrow.

MLOps Engineer at a Tech Company (Remote, EMEA) — Mid-Level

09:00 — Async standup. Overnight, a model training job failed (data quality issue: missing values in feature feed). You've already diagnosed and restarted with data validation checks added.

10:00 — 1:1 with your manager. You're proposing a feature store architecture redesign to support multi-team feature sharing. She approves—this will accelerate model development company-wide.

10:30 — Design the new feature store: shared infrastructure, governance for data quality, feature lineage tracking, and cost allocation. Document architecture.

12:00 — Lunch + consultation call with a data science team. They want to deploy a new recommendation model. You review the model: accuracy looks good, but inference latency is 500ms (too slow for real-time). Recommend model optimisation (quantisation, distillation). Offer to help implement.

13:00 — Work on the recommendation model optimisation. Apply knowledge distillation: train a smaller student model using the larger teacher model's predictions. Result: 50ms latency (10x speedup), 99.5% accuracy (acceptable loss).

14:30 — Design a monitoring dashboard for the recommendation model. Include: accuracy, latency, throughput, inference cost, and user satisfaction (A/B test results).

15:30 — Work on A/B testing infrastructure. Implement a framework for running experiments: traffic splitting, metric collection, and statistical significance testing. This enables safe model rollouts.

16:30 — Mentor a junior MLOps engineer. Review their feature store implementation. Check: schema design, data quality validation, and documentation. Suggest improvements.

17:00 — Wrap up. Monitor all ML systems (no issues). Plan next week: start feature store architecture migration project.

Market specifics

MLOps demand in SA is emerging. Tech companies (Takealot), fintechs using fraud detection ML, and data-driven startups are beginning to hire. Government and large enterprises have limited ML adoption, so limited MLOps demand.

The MLOps market in SA is tiny but growing fast—one of the fastest-growing specialisations globally. Companies doing MLOps are typically well-funded and pay premiums. Remote work opportunities are excellent: tech companies globally aggressively hire SA-based MLOps engineers.

MLOps requires dual expertise (ML + DevOps), making it harder to find talent. This creates opportunity for career changers willing to invest 24–36 months.

SA-specific resources

Q: Do I need to be a strong data scientist to become an MLOps engineer?

Not required. You need to understand ML concepts (training, validation, model evaluation), but you don't need to publish papers or compete on Kaggle. Many successful MLOps engineers come from DevOps backgrounds and learn ML on the job. Data engineer or DevOps background is more valuable than deep ML.

Q: Is MLOps harder than traditional DevOps?

Different, not necessarily harder. You need both DevOps skills (infrastructure, automation, monitoring) and ML knowledge (model evaluation, feature engineering, retraining). The combination is intellectually demanding. If you're not interested in both, MLOps might not be for you.

Q: How long does it really take to get a first MLOps job?

24–36 months if starting from zero. Timeline: 6 months ML fundamentals + 6 months DevOps fundamentals + 12 months MLOps specialisation + 6 months job search/portfolio. If you already have DevOps background: 12–18 months. If you already have ML background: 12–18 months.

Q: Which cloud platform should I focus on for MLOps?

AWS has the largest market share (SageMaker). If targeting global companies, start with AWS. GCP (Vertex AI) is simpler and more integrated. Databricks is rapidly growing and excellent for data engineering + ML. Ideal: learn AWS first, then Databricks or GCP.

Q: What's the difference between an MLOps engineer and a data engineer?

Data engineer: focuses on data infrastructure, pipelines, and quality. MLOps engineer: focuses on ML pipelines, model deployment, and operations. Data engineers build the data layer; MLOps engineers use it to serve ML models. Both are essential and complementary.

Career path guide for MLOps engineers | Last updated 2026-05-02 | ZAR baseline: R18/$1 USD For updates and job leads, see IT Career Roadmap