ML / AI Engineer Assessment
Train, deploy, and operate machine learning models on cloud
The ML / AI Engineer assessment covers 6 domains - from data preparation and model training to MLOps and generative AI. Choose your platform and get a verified score that reflects where the industry is heading.
What does a ML / AI Engineer do?
ML / AI Engineers bridge the gap between data science and production infrastructure. They build the pipelines that train, evaluate, and deploy models at scale - and keep those models healthy in production through monitoring, retraining triggers, and A/B testing.
The rise of generative AI has expanded this role significantly. Engineers who can operationalise foundation models, implement RAG pipelines, and evaluate LLM outputs are increasingly in demand globally, including for remote roles from Africa.
What Kloud Safari tests for ML / AI Engineer
The assessment covers 6 domains across scenario-based questions designed around real trade-off decisions.
ML Problem Framing
Supervised vs unsupervised learning, regression vs classification trade-offs, choosing evaluation metrics, and identifying when ML is and is not the right solution.
Data Preparation
Feature engineering, managed feature stores, data labelling workflows, handling class imbalance, and detecting bias in training datasets across cloud ML platforms.
Model Training & Tuning
Managed training jobs (SageMaker / Azure ML / Vertex AI), built-in algorithms vs bring-your-own-container, distributed training strategies, and hyperparameter optimisation.
Deployment & Inference
Real-time endpoints, batch transform, async inference, multi-model endpoints, edge deployment, and latency vs cost trade-offs at inference time across cloud platforms.
MLOps & Monitoring
ML pipeline orchestration, model registries, data and model drift detection, A/B testing endpoints, retraining triggers, and model versioning strategies.
Generative AI on Cloud
Foundation model selection (Bedrock / Azure OpenAI / Vertex AI), RAG architecture design, fine-tuning vs prompt engineering trade-offs, vector databases, and evaluating LLM output quality.
Key skills covered
Which level will you be placed at?
Every engineer is placed across four levels based on their assessment responses.
Pre-Junior
Core service awareness, limited hands-on. Learning the fundamentals.
Junior
Can build basic solutions independently within defined patterns.
Mid-Level
Designs multi-service, multi-AZ systems. Navigates trade-offs confidently.
Senior
Architects at org scale. Sets standards. Mentors other engineers.
What you get after the assessment
Verified Readiness Score
An overall percentage score plus section-by-section breakdown across each domain. Share your public profile with recruiters.
Gap Report
A ranked list of the specific skill gaps holding you back, with an estimate of how long each will take to close.
Week-by-Week Roadmap
A personalised plan of certifications, projects, and courses - ordered by impact - to reach the next level.
Assessment FAQ
Is the assessment free?
Yes. The assessment, score, gap report, and roadmap are entirely free.
Which cloud does the ML Engineer assessment cover?
You choose - AWS (SageMaker / Bedrock), Azure (Azure ML / Azure OpenAI), or GCP (Vertex AI). Questions are tailored to your chosen platform.
Does the assessment cover generative AI and LLMs?
Yes. The Generative AI domain covers foundation model deployment, RAG architectures, fine-tuning trade-offs, and LLM evaluation across all three cloud providers.
Do I need a statistics or PhD background?
No. The assessment focuses on engineering skills - building, deploying, and operating ML systems on cloud - not academic ML theory or advanced mathematics.
What is the difference between a Data Scientist and an ML Engineer?
Data Scientists focus on model development and analysis. ML Engineers focus on the infrastructure to train, deploy, and serve models reliably at scale. This assessment tests the engineering side.
Ready to find your level?
Free · No card required · Results and roadmap in 20 minutes
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