AI Architect Skill Matrix
Self-assess across the 8 domains that define an AI Architect. Your ratings save locally in this browser — no account required.
Skills rated
0 / 49
Architect maturity
0%
Average level
0.0 / 5
Foundations stage. Pick 2 domains and go deep.
Maturity by domain
ML & Deep Learning Fundamentals0.0 · 0/6
LLM & Generative AI Systems0.0 · 0/7
MLOps & Production AI0.0 · 0/6
Data Engineering for AI0.0 · 0/6
AI System Design at Scale0.0 · 0/6
AI Cloud Infrastructure0.0 · 0/6
Responsible AI, Safety & Governance0.0 · 0/6
Technical Leadership & Architecture Influence0.0 · 0/6
Full ratings
🧮 ML & Deep Learning Fundamentals
- Supervised learning trade-offs: Not yet
- Deep learning architectures: Not yet
- Optimization & training dynamics: Not yet
- Evaluation methodology: Not yet
- Interpretability & probing: Not yet
- Build vs. fine-tune vs. buy: Not yet
🤖 LLM & Generative AI Systems
- Transformer internals: Not yet
- Pre-training, SFT, RLHF, DPO: Not yet
- Retrieval-augmented generation: Not yet
- Agentic systems & tool use: Not yet
- LLM evaluation & guardrails: Not yet
- Inference optimization: Not yet
- Prompt & context architecture: Not yet
⚙️ MLOps & Production AI
- Model deployment patterns: Not yet
- Monitoring & drift detection: Not yet
- Feature platforms: Not yet
- Experimentation infrastructure: Not yet
- Model governance & lineage: Not yet
- Inference cost engineering: Not yet
🗄️ Data Engineering for AI
- Data architecture (lakehouse, warehouse): Not yet
- Streaming & event-driven pipelines: Not yet
- Labeling & weak supervision: Not yet
- Data quality & contracts: Not yet
- Vector stores & embeddings infra: Not yet
- PII handling & privacy-preserving prep: Not yet
🏗️ AI System Design at Scale
- Distributed training: Not yet
- Serving architecture: Not yet
- Latency budget engineering: Not yet
- Scalability & capacity planning: Not yet
- Resilience & graceful degradation: Not yet
- Multi-tenant AI platforms: Not yet
☁️ AI Cloud Infrastructure
- GPU/accelerator fleet management: Not yet
- Kubernetes for AI workloads: Not yet
- AI networking (InfiniBand, RDMA, NVLink): Not yet
- Storage for AI: Not yet
- Cost architecture across clouds: Not yet
- Managed AI services trade-offs: Not yet
🛡️ Responsible AI, Safety & Governance
- Risk frameworks (NIST AI RMF, ISO 42001): Not yet
- Regulatory landscape (EU AI Act, GDPR, sectoral): Not yet
- Fairness & bias evaluation: Not yet
- Red-teaming & adversarial testing: Not yet
- Privacy-preserving ML: Not yet
- AI incident response: Not yet
🧭 Technical Leadership & Architecture Influence
- Architecture decision records & RFCs: Not yet
- Executive & stakeholder communication: Not yet
- Cross-team influence without authority: Not yet
- Mentorship & technical hiring: Not yet
- Vendor & build/buy evaluation: Not yet
- Roadmapping & sequencing AI bets: Not yet
Rate yourself
Supervised learning trade-offs
Bias-variance, regularization, model capacity vs. data size, calibration. Choosing classical vs. deep approaches for a given problem.
Deep learning architectures
CNNs, RNNs, Transformers, MoE. Knowing when each is the right tool and what its scaling pathologies look like.
Optimization & training dynamics
Loss landscapes, optimizer choice (AdamW, Lion, Shampoo), learning-rate schedules, gradient pathologies, mixed precision.
Evaluation methodology
Choosing metrics that align with business objective, holdout discipline, leakage detection, statistical significance of A/B results.
Interpretability & probing
SHAP, integrated gradients, attention probing, mechanistic interpretability basics. Knowing where each technique misleads.
Build vs. fine-tune vs. buy
Deciding between training from scratch, fine-tuning, prompting an API, or buying a vendor solution — given cost, data, and IP constraints.
Next step
Validate your self-assessment with the architect-level knowledge quizzes.
Go to Assessments →