AI Engineer — Role-Based Learning Hub
A modular collection of role-specific, project-driven curriculum tracks. Each track targets a distinct job description, stands alone as a complete learning path, and opens as its own book with a focused sidebar and search.
Tracks
| Track | Focus |
|---|---|
| LLM Inside-Out Mastery | Vocabulary → transformers → model selection → local inference → serving → RAG → agents → eval → security → startup |
| LLM Inference Engineer | Tokenization → transformers → training → RAG → inference serving |
| CV Engineer | Classical CV, PyTorch/TF, detection & segmentation, MLOps |
| Senior ML Engineer | Feature stores, retrieval, ranking, recsys, experimentation, MLOps |
| Model Accuracy & AI Performance | Framework internals, quantization, compilers, NPU deployment, profiling |
| Head of Software Engineering — GPU | GPU architecture, CUDA, compilers, drivers, runtimes, scheduling |
| Rack Management — Senior/Staff SWE | Rack-scale AI systems, Redfish/IPMI/SNMP, BMC/PDU/CDU/PCIe, provisioning, K8s, telemetry, firmware/RAS, secure multi-tenancy |
| AI Specialist | Operational digital twin (JD1) + inference optimization & accelerators (JD2) |
| Apache Committer / PMC Engineer | ASF governance, JVM systems, review culture, release engineering |
| Security Engineer | AppSec, OS & sandboxing, cloud, vuln research, IR, AI/agent security |
| Principal Data Infrastructure Engineer | Distributed data platform: Kafka/Kinesis, Flink, Spark, EMR, Iceberg/Delta/Hudi, Trino/Athena, Cassandra, Scala/Cats/ZIO, governance & reliability |
| Red Team Engineer | Adversary emulation, engagement lifecycle, OS internals, AD/Kerberos, C2, cloud/container, social engineering, RE, purple team — Mandiant consulting track |
| Principal Azure Cloud Engineer & Architect | ARM control plane, Terraform/CDKTF IaC, Entra ID/OAuth2/OIDC/JWT, RBAC & Policy, landing zones, VNet/NSG/routing, ACR/AKS, CI/CD + OIDC federation, APIM, Service Bus/Event Grid, Functions/Durable, Key Vault/Managed Identity, KQL, reliability & FinOps |
| Senior AI Engineer (LLMs/VLMs/Agents) | From scratch: tokenizer → transformer/attention → autograd → VLMs → LoRA/QLoRA/quantization → RLHF/DPO → sampling & constrained decoding → vLLM-class serving (PagedAttention) → distributed training → RAG/ANN → agents & multi-agent → neurosymbolic → embodied/VLA → eval/guardrails → MLOps (MLflow) → CUDA → capstone |
| Agentic AI Engineer (Infra & Platforms) | Agent infrastructure from scratch: reliability/cost math → ReAct/ReWOO loop → tool calling & JSON-Schema → MCP servers → context engineering → RAG + GraphRAG/LightRAG/RAPTOR → multi-agent → durable execution (Temporal-class) → sandboxing → prompt-injection defense → LLM-as-judge evals → async services → multi-tenancy → cost/observability → coding agents → voice → enterprise-platform capstone. Part II — Frameworks Deep Dive: LangGraph, CrewAI, AWS Bedrock AgentCore, OpenAI Agents SDK, Google ADK, AutoGen/Microsoft Agent Framework, and the Amazon Bedrock foundation-model platform. Part III — Cloud AI Platforms, MLOps & Production Infra: SageMaker/Vertex AI/Azure ML, MLOps (tracking/registry/CI-CD/drift), data & feature engineering (feature stores/Dataflow/BigQuery ML/Databricks), Kubernetes/OKE SRE. Part IV — GenAI Frameworks & ML Foundations: LangChain core, Hugging Face, Cohere, ML/DL foundations (TF/PyTorch, supervised/unsupervised/RL). Part V — Eval-Driven Development for software-delivery agents. Every phase is a faithful stdlib miniature + four principal-depth deep-dive docs (Deep Dive / Principal Deep Dive / Core Contributor Notes / Staff Engineer Notes) |
How to Use This Hub
- Pick the track that matches your target role and open its book.
- Start with the track's Overview for the full roadmap and weekly schedule.
- Work through phases sequentially — each phase gates the next.
- Use each track's Interview Prep and System Design chapters as running references.
- Use the ← All Roles link at the top of any track's overview to come back here.
Cross-Track Skills (Shared Foundations)
Regardless of which track you pursue, these skills underpin every role:
- Python & systems fluency — typing, packaging, profiling, concurrency.
- Linear algebra, probability, and optimization — the math beneath every model.
- PyTorch internals — autograd, modules, data pipelines, distributed basics.
- Production engineering — testing, CI, containers, observability.
- System design — every track ends in design walkthroughs for its domain.
Published · commit
1597670· 2026-07-16 01:19 UTC