🚀 DevOps AI Engineer Roadmap

A practical, project-first roadmap for DevOps, Platform, SRE, and Cloud engineers who want to become AI Infrastructure / AI Platform Engineers.

This repository connects traditional infrastructure skills—Linux, containers, Kubernetes, Terraform, CI/CD, observability, and security—with the modern AI platform stack: local LLMs, GPU scheduling, inference serving, RAG systems, agents, MCP, and AI safety.

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📌 Table of Contents

• Who This Is For
• What You Will Build
• Repository Map
• Recommended Learning Paths
• Core Curriculum
• Capstone Projects
• Quick Start
• Suggested Improvements for This Repo
• Contributing

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👤 Who This Is For

This roadmap is designed for learners who already know at least one of these areas and want to move toward AI platform work:

• DevOps engineers who want to deploy and operate LLM-powered systems.
• SREs who want to build AI-assisted observability, alerting, and remediation workflows.
• Platform engineers who want to run GPU-backed inference platforms on Kubernetes.
• Cloud engineers who want to provision AI-ready infrastructure using Terraform and Kubernetes.
• Backend engineers who want to understand RAG, model serving, and production AI operations.

If you are new to DevOps, start with the Fundamentals modules and the 12 Days Series before jumping into the advanced projects.

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🧪 What You Will Build

By following this repository, you will practice building:

• Local LLM runtimes with Ollama and llama.cpp-style workflows.
• Secure Kubernetes manifests for AI workloads.
• RAG pipelines with vector databases and local inference.
• AI-assisted log analysis and incident triage services.
• GPU-aware Kubernetes inference platforms with autoscaling.
• Agentic DevOps tools that interact with infrastructure safely.
• Observability and guardrail patterns for LLM applications.

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🗂️ Repository Map

Path	Purpose
ROADMAP.md	24-week phased roadmap from infrastructure fundamentals to AI security and AIOps.
QUICKSTART.md	Fastest path to run a local lab and start building.
STUDY_MATERIALS.md	Curated study resources, official docs, labs, portfolio ideas, and milestones.
12-days-series/	Daily hands-on learning sequence for core DevOps + AI skills.
fundamentals/	Linux, networking, Docker, Kubernetes, CI/CD, Terraform, storage, and Python automation notes.
prompt-engineering/	Prompting patterns, structured outputs, tool use, and evaluation concepts.
local-llms/	Local model runtimes, model formats, quantization, and performance tuning.
kubernetes-ai/	GPU Operator, inference serving, KServe, vLLM, KEDA, and Kubernetes AI operations.
rag-vector-db/	Retrieval-augmented generation, embeddings, Qdrant, ChromaDB, pgvector, and vector search.
ai-agents/	Agent design, tool calling, safe automation, and multi-agent workflows.
mcp/	Model Context Protocol concepts and server patterns.
observability-aiops/	OpenTelemetry, AI tracing, logs, metrics, and incident automation.
ai-security/	Prompt injection, guardrails, secrets, network policy, and LLM security risks.
projects/	Portfolio-grade capstone projects and runnable examples.
diagrams/	Mermaid architecture diagrams and rendering instructions.

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🧭 Recommended Learning Paths

🟢 Beginner: DevOps to AI Foundations

1. Read QUICKSTART.md.
2. Complete 12-days-series/day01-linux-containers.
3. Complete the Kubernetes and Python automation days in 12-days-series/.
4. Read local-llms/ and run one local model.
5. Build the AI DevOps Copilot project.

🟡 Intermediate: Platform Engineer to AI Platform Engineer

1. Review fundamentals/kubernetes-basics.md and fundamentals/terraform.md.
2. Study kubernetes-ai/ and local-llms/.
3. Build the Local RAG Assistant.
4. Deploy the Kubernetes AI Platform.
5. Add observability from observability-aiops/.

🔴 Advanced: SRE / AIOps / Security Specialist

1. Study ai-agents/, mcp/, and ai-security/.
2. Build the AI Log Analysis Pipeline.
3. Build the AI SRE Agent.
4. Add approval gates, audit logs, and rollback controls.
5. Document security tradeoffs using STUDY_MATERIALS.md.

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🧱 Core Curriculum

Module	Topic	Key Outcomes
1	Fundamentals	Linux, networking, Docker, Kubernetes basics, CI/CD, Terraform, storage, and automation.
2	Prompt Engineering	Prompt patterns, context management, structured outputs, tool calls, and evaluations.
3	Local LLMs	Ollama, model formats, quantization, local inference, and CPU/GPU tradeoffs.
4	Hugging Face	Transformers, datasets, model registries, tokenizers, and model workflows.
5	Kubernetes AI	GPU scheduling, NVIDIA GPU Operator, vLLM, KServe, KEDA, and inference operations.
6	RAG & Vector Databases	Embeddings, chunking, Qdrant, ChromaDB, pgvector, hybrid retrieval, and evaluation.
7	AI Agents	Tool use, autonomous workflows, guardrails, and operational agents.
8	Model Context Protocol	MCP architecture, server design, tool/resource exposure, and client integration.
9	Orchestration	n8n-style workflows, event routing, approvals, and automation pipelines.
10	Observability & AIOps	Metrics, logs, traces, LLM observability, alerting, and incident intelligence.
11	AI Security	Prompt injection, secrets, data leakage, network policy, and guardrail design.
12	Capstone Projects	End-to-end portfolio projects that combine infrastructure, AI, and operations.

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🛠️ Capstone Projects

Project	What it teaches	Start here
Local RAG Assistant	Streamlit, Qdrant, embeddings, Ollama, and local RAG loops.	projects/local-rag-assistant/
AI DevOps Copilot	Click CLI, local model prompts, Kubernetes YAML, Terraform generation, and safe fallbacks.	projects/ai-devops-copilot/
AI Log Analysis Pipeline	FastAPI, Vector log routing, LLM-based diagnosis, and simulation mode.	projects/ai-log-analysis-pipeline/
Kubernetes AI Platform	GPU inference, vLLM/KServe manifests, KEDA scaling, and Terraform GPU infrastructure.	projects/kubernetes-ai-platform/
AI SRE Agent	Alertmanager webhooks, Kubernetes diagnostics, remediation decisions, and safe auto-healing.	projects/ai-sre-agent/

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⚡ Quick Start

Option 1: Read and choose a path

cat QUICKSTART.md
cat ROADMAP.md
cat STUDY_MATERIALS.md

Option 2: Run the AI DevOps Copilot

cd projects/ai-devops-copilot
python -m pip install -r requirements.txt
python copilot.py generate kubernetes --prompt "nginx deployment with 3 replicas"

Option 3: Run project tests

python -m pytest projects/*/tests -q

Some labs use Docker, Kubernetes, GPUs, Ollama, or model downloads. When those services are unavailable, several examples include local simulation fallbacks so learners can still inspect the workflow.

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🧰 Recommended Local Tooling

Install these tools as you progress through the roadmap:

• Python 3.10+ or 3.11+
• Docker and Docker Compose
• kubectl, Kind or Minikube
• Terraform
• Ollama or another local model runtime
• A code editor with Markdown and Mermaid preview support
• Optional: NVIDIA GPU drivers and CUDA toolkit for GPU labs

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✅ Quality Checks

The repository includes tests and validation workflows for learning projects and examples:

python -m pytest projects/*/tests -q
python -m flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
python -m black --check $(git ls-files '*.py')

The GitHub Actions workflow also validates Markdown links, YAML syntax, Dockerfiles, and security scanning where supported.

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💡 Suggested Improvements for This Repo

These are the highest-value next improvements to make the repository more useful and production-like:

Documentation and learning experience

• Add screenshots or short GIF demos for each capstone project.
• Add architecture diagrams to each project README using Mermaid and exported PNG/SVG files.
• Add a glossary for AI infrastructure terms such as KV cache, quantization, embeddings, HNSW, MCP, and guardrails.
• Add beginner, intermediate, and advanced issue labels to guide new contributors.
• Add a progress tracker template that learners can copy into their own fork.

Project reliability

• Add .env.example files for every runnable project.
• Add health checks to all Docker Compose services.
• Add Docker Compose profiles for CPU-only and GPU-enabled modes.
• Add smoke-test scripts for each project under a shared scripts/ directory.
• Add Make targets at the repository root for test, lint, format, links, and docs.

AI platform depth

• Add a vLLM OpenAI-compatible inference demo with request/response examples.
• Add a RAG evaluation notebook or script that scores retrieval quality before and after chunking changes.
• Add a model-serving benchmark comparing Ollama, llama.cpp, and vLLM on the same prompt set.
• Add examples for prompt-injection testing and output validation.
• Add an MCP server example that exposes safe read-only Kubernetes diagnostics.

Security and production readiness

• Add Kubernetes NetworkPolicy examples for vector databases and inference services.
• Add RBAC examples for read-only agents and approval-gated remediation agents.
• Add secret-management examples using environment variables, sealed secrets, or cloud secret managers.
• Add threat models for the RAG assistant, AI SRE agent, and DevOps copilot.
• Add audit logging for any agent action that reads infrastructure state or proposes remediation.

Community and maintainability

• Replace placeholder community links with real community channels when available.
• Add a CODEOWNERS file for project/module ownership.
• Add pull request templates for docs, labs, and code changes.
• Add a release checklist for major curriculum updates.
• Add a public project board or milestone list for the next roadmap improvements.

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🤝 Contributing

Contributions are welcome. Good first contributions include:

• Fixing broken links or typos.
• Improving lab instructions.
• Adding diagrams or screenshots.
• Adding tests for project examples.
• Adding security notes and production hardening guidance.

Before opening a PR, read CONTRIBUTING.md and keep changes focused.

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📚 More Study Resources

Use these files together:

• QUICKSTART.md for the fastest hands-on entry point.
• ROADMAP.md for the 24-week learning plan.
• STUDY_MATERIALS.md for curated external resources and portfolio ideas.
• TROUBLESHOOTING.md for common setup and runtime issues.
• BADGES.md for learner achievement ideas.

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📄 License

This project is licensed under the MIT License.

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Build the platform. Operate the intelligence. Document everything.