Engineering Learning Paths

Structured milestone roadmaps and hands-on laboratory exercises designed to build mastery across cloud architecture, Kubernetes orchestration, SRE, and AI infrastructure.

TRACK 01 · FOUNDATIONS

Cloud Infrastructure & DevOps

Master the core engineering pillars required to build, package, and automate cloud systems with zero downtime.

M1 Linux Systems, Kernel Parameters & Bash Automation
M2 Multi-Stage Docker Containerization & Distroless Packaging
M3 AWS Core Infrastructure (VPC, Subnets, IAM, EC2, S3)
M4 Declarative Terraform Modules & Remote State Locking
TRACK 02 · ORCHESTRATION

Advanced Kubernetes & GitOps

Design and operate multi-tenant Kubernetes clusters with automated GitOps delivery and zero-trust service mesh.

M1 K8s Control Plane Internals, Scheduling & CNI Networking
M2 Production Manifests, HPA v2, PDBs & Resource Quotas
M3 ArgoCD Declarative GitOps & Canary Rollout Strategies
M4 Istio Service Mesh mTLS & Traffic Management
TRACK 03 · RELIABILITY

Site Reliability Engineering (SRE)

Build resilient production systems, manage error budgets, and orchestrate telemetry across distributed services.

M1 SLI/SLO Math, Error Budgets & Multi-Window Burn Rate Alerts
M2 Prometheus PromQL Mastery & Exporter Architecture
M3 OpenTelemetry Distributed Tracing & Span Context Propagation
M4 Blameless Incident Management & Chaos Engineering
TRACK 04 · NEXT-GEN INFRA

AI Infrastructure & LLMOps

Architect and scale GPU compute clusters for LLM inference, embedding pipelines, and vector retrieval.

M1 NVIDIA GPU Drivers, CUDA Runtimes & K8s GPU Operator
M2 vLLM Inference Serving, PagedAttention & Continuous Batching
M3 Distributed Vector Databases (Milvus, Qdrant) & Sharding
M4 Prompt Evaluation, LLM Guardrails & Token Observability