Roadmap

Building AI that actually works in production is very different from training models or running demos. This Roadmap is a practical guide to becoming an AI engineer who understands systems, infrastructure, performance, and cost — not just models. It's designed to help you grow from fundamentals to production-grade AI systems, step by step.

Milestone

Level 8 - AI Systems Architecture, Safety & Organizational Scale

Final tier. Stopped being an AI engineer and became someone who designs, governs, and scales AI systems end-to-end. Systems thinking replaces component thinking. AI problems become people, risk, and incentives problems.

Skills:
  • AI Platform Architecture
  • Systems Thinking
  • Failure Mode Analysis
  • Blast Radius Assessment
  • AI Reliability Engineering
  • Canary Deployments
  • Shadow Inference
  • Fallback Chain Design
  • AI Safety Fundamentals
  • Threat Modeling
  • Prompt Injection Defense
  • Jailbreak Prevention
  • Governance & Compliance
  • Auditability
  • Human-in-the-Loop
  • HITL Workflow Design
  • Organizational Scale
  • Platform Strategy
  • Strategic Decision Making
  • Vendor Risk Assessment
Milestone

Level 7 - Cost Optimization (AI Systems Economics)

Transitioned from builder to owner thinking. Mastered AI systems economics, token math, model selection strategies, context optimization, caching, batching, and cost guardrails. Can design, justify, and defend AI system costs. Most startups don't fail from bad models—they fail from bad unit economics.

Skills:
  • Cost Surface Analysis
  • Token Economics
  • Cost Calculation Math
  • Model Selection Strategy
  • Smart Model Routing
  • Context Optimization
  • Prompt Compression
  • Caching Strategies
  • Hit Rate Optimization
  • Batching Economics
  • Async Inference
  • Hardware Economics
  • GPU Cost Analysis
  • Cost Guardrails
  • Product-Level Cost Awareness
Milestone

Level 6 - Fine-Tuning for Control

Stopped asking models nicely and started controlling their behavior. Mastered LoRA/QLoRA for efficient fine-tuning, behavior plugins architecture, data curation, and evaluation. Fine-tuning is NOT for adding facts—it's for controlling behavior predictably, consistently, cheaply, and safely.

Skills:
  • Fine-Tuning Strategy
  • LoRA Fundamentals
  • QLoRA Quantization
  • Adapter Patterns
  • Behavior Plugin Architecture
  • Instruction Dataset Creation
  • Preference Dataset Curation
  • Schema Enforcement
  • Safety Control
  • Style Shaping
  • PyTorch Training Loop
  • HuggingFace PEFT
  • Training Monitoring
  • Overfitting Prevention
  • Inference Serving
Milestone

Level 5 - Retrieval Systems (RAG Done Right)

Mastered RAG systems—where 70-80% of AI apps fail. Understood information retrieval, embeddings, chunking strategies, vector databases, hybrid search, re-ranking, and confidence scoring. Built systems that are accurate, explainable, cheap, and safe. RAG is not LLM + vector DB; it's an information retrieval system with an LLM attached.

Skills:
  • Information Retrieval Fundamentals
  • Precision & Recall Optimization
  • Lexical vs Semantic Search
  • Embedding Model Selection
  • Advanced Chunking Strategies
  • Vector Database Architecture
  • Hybrid Search Implementation
  • Cross-Encoder Re-ranking
  • Context Construction
  • Confidence Scoring
  • Hallucination Prevention
  • RAG Evaluation Metrics
  • Citation & Grounding
  • Retrieval Failure Handling
Milestone

Level 4 - Model Serving & Inference at Scale

Crossed from learning AI to being paid for AI. Mastered vLLM, continuous batching, GPU fundamentals, multi-model serving, streaming, cost thinking, and deployment. Can serve LLMs efficiently, explain why they're slow or expensive, and design systems that don't melt GPUs. Inference pays salaries—this level is where that value emerges.

Skills:
  • vLLM Framework
  • Continuous Batching
  • PagedAttention
  • KV Cache Management
  • GPU Fundamentals
  • VRAM Optimization
  • Batch Size Tuning
  • Streaming Responses
  • Multi-Model Serving
  • Model Routing
  • CPU vs GPU Trade-offs
  • Cold Start Optimization
  • Throughput Maximization
  • Cost Per Token Analysis
  • Production Deployment
Milestone

Level 3 - Practical AI Fundamentals

AI stops being magic and becomes a system you can reason about. Mastered PyTorch, HuggingFace, LLM concepts, embeddings, inference parameters, and production failure modes. Can run, inspect, and debug LLMs confidently from infra-first perspective.

Skills:
  • LLM Concepts & Tokenization
  • PyTorch Fundamentals
  • Tensor Operations
  • HuggingFace Transformers
  • Model Loading & Inference
  • Embeddings & Semantic Search
  • Inference Parameters
  • Temperature & Top-P Control
  • Memory Management
  • GPU/CPU Inference
  • Performance Profiling
  • Production Failure Modes
  • Cold Start Optimization
  • Error Handling
Milestone

Level 2 - Backend Engineering for AI

Mastered FastAPI, API design, PostgreSQL, Redis, authentication, rate limiting, observability, and failure handling. Builds AI-ready production backends that safely host models, RAG pipelines, and scale gracefully. Backend as control plane for AI.

Skills:
  • FastAPI Framework
  • Async/Await Programming
  • Dependency Injection
  • API Design for AI
  • Streaming Responses
  • PostgreSQL & ORM
  • Redis Caching
  • Rate Limiting
  • Authentication
  • JWT & Security
  • Backpressure Handling
  • Structured Logging
  • Prometheus Metrics
  • Observability & Tracing
  • Circuit Breaker Pattern
  • Failure Handling
Milestone

Level 1 - Docker & Cloud Fundamentals

Mastered containerization and cloud infrastructure. Transformed from someone who runs code into someone who ships systems. Containerized backends to cloud with Infrastructure as Code, security, and cost awareness.

Skills:
  • Docker Fundamentals
  • Dockerfile Optimization
  • Multi-stage Builds
  • Docker Compose
  • Cloud Architecture
  • Azure Fundamentals
  • Network Security Groups
  • Cloud Networking
  • Cloud Storage
  • Terraform & IaC
  • Infrastructure Provisioning
  • Cost Estimation
  • Containerized Deployments
  • Reverse Proxy Configuration
  • Production Mindset
Milestone

Level 0 - Computer + Infra Foundations

A deep, beginner-safe guide to the foundational layer that separates true AI builders from API callers. Mastered computer architecture, networking, Linux, Git, Bash scripting, and compute concepts. Successfully deployed production-like systems and achieved senior-level systems thinking.

Skills:
  • Systems Thinking
  • Linux Fundamentals
  • Bash Scripting & Automation
  • Git & Version Control
  • Networking Fundamentals
  • HTTP/REST/gRPC
  • Load Balancing
  • CPU/GPU Architecture
  • Async I/O & Concurrency
  • Bottleneck Identification
  • FastAPI Deployment
  • Nginx & Reverse Proxy
  • Production Workflows
  • Architecture Design