The Generative AI wave isn’t coming — it’s already here. Companies are scrambling to operationalize LLMs (Large Language Models) safely, securely, and at scale. That’s where LLMOps Engineers come in.
If you’re a DevOps Engineer today, you already have half the foundation built — cloud, automation, and operational excellence. But the other half? That’s a whole new skill set in MLOps, LLM orchestration, GenAI agents, and AI governance.
Here’s a practical, month-by-month roadmap to take you from DevOps to LLMOps in under a year.
Stage 1 – Strengthen Your DevOps & Cloud Core (Months 1–2)
Before you tackle AI workloads, you need airtight operational skills.
Cloud Platforms: Deepen AWS/GCP/Azure expertise. Focus on IAM, networking, cost control.
Containers & Orchestration: Docker best practices, Kubernetes deployments, RBAC.
CI/CD Pipelines: Automate deployments with GitHub Actions, GitLab CI, or Jenkins.
Observability: Master Prometheus, Grafana, and distributed logging.
Mini Project: Deploy a multi-service app with CI/CD, monitoring, and RBAC on Kubernetes.
Stage 2 – Enter the MLOps Arena (Months 3–4)
Understand how models are trained, deployed, and maintained.
MLOps Tools: MLflow for tracking, DVC for versioning.
Serving Models: FastAPI + Docker, Seldon Core, BentoML.
Pipelines: Automate training, evaluation, deployment.
Monitoring: Detect drift, track inference performance.
Mini Project: Deploy an ML model with MLflow registry, serve it via FastAPI, monitor it in Grafana.
Stage 3 – Specialize in LLMOps & Agent Workflows (Months 5–6)
Now it gets exciting — orchestrating GenAI at scale.
LLM APIs: OpenAI, Anthropic, Cohere, Azure OpenAI.
Agent Frameworks: LangChain, LlamaIndex, LangGraph.
Vector Databases & RAG: FAISS, Pinecone, Weaviate.
Optimization: LoRA fine-tuning, quantization, caching.
Hybrid Orchestration: Multi-LLM routing and fallback strategies.
Mini Project: Build a multi-agent RAG system with LangGraph + Pinecone that uses two different LLM providers for redundancy.
Stage 4 – Governance, Security & Responsible AI (Month 7)
The part most engineers overlook — and the one employers value most.
Governance: NIST AI RMF, GDPR, EU AI Act basics.
Responsible AI: Fairness, explainability, bias detection.
Security: Secure API gateways, data encryption, sandboxing.
Model Safety Monitoring: Detect hallucinations, toxicity, performance drops.
Mini Project: Build a governed LLMOps pipeline with IAM, encryption, hallucination detection, and audit logging.
Capstone Project – Your Portfolio Booster
“Secure, Governed, Multi-LLM Agent Platform”
Multi-agent orchestration with LangGraph
Hybrid LLM integration (OpenAI + Anthropic)
RAG with Pinecone
MLflow-tracked LoRA models
Governance and monitoring baked in
This is the kind of end-to-end, security-conscious AI platform employers are hiring for right now.
Why This Works
You build on what you already know (DevOps skills)
You stack MLOps before LLMOps (no skipping steps)
You end with governance (where real enterprise adoption happens)
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