Most DevOps engineers hit a ceiling: you know Kubernetes, Terraform, CI/CD — but the market is shifting. Teams need engineers who can build and operate AI agent infrastructure, not just cloud infrastructure. The good news? Your DevOps background is exactly the right foundation. The bad news? Most "AI courses" teach you to call APIs, not to build production systems.
This roadmap is different. It assumes you already know Linux, containers, Git, and cloud. It skips the hype and gives you the exact patterns we ship at Tayoca for FinOps agents, incident-response bots, and GitOps automation — the stuff that runs in production at fintech and SaaS platforms.
Why Your DevOps Background Is a Superpower
Every production AI system needs:
| DevOps Skill | AI System Equivalent |
|---|---|
| Kubernetes / Containers | Self-hosted model serving (vLLM, Ollama), GPU scheduling, batch inference |
| Observability (Prometheus/Grafana) | Agent tracing, token usage dashboards, latency SLOs, eval metrics |
| Incident Response | Agent failure modes, hallucination detection, fallback chains |
| Security / RBAC | Prompt injection defense, data leakage prevention, tenant isolation |
| CI/CD & GitOps | Eval-driven development, automated regression on prompt/model changes |
| Infrastructure as Code | Reproducible agent environments, versioned prompts & tools |
Phase 1: Foundations (Month 1–2) — No Certificates, Build Instead
Core Concepts to Internalize
- LLM Architecture: Transformer basics, attention, context windows, tokenization
- RAG Pipeline: Embedding models, vector DBs (pgvector, Qdrant, Pinecone), chunking strategies, retrieval evaluation
- Agent Patterns: ReAct, Plan-and-Execute, Reflexion, Multi-agent orchestration
- Tool Use / Function Calling: Schema design, parallel execution, error handling, streaming
- Evaluation: LLM-as-judge, deterministic evals, human preference alignment, regression testing
Hands-On: Build These 3 Micro-Projects
- Local RAG Chatbot: Ingest your own docs (PDF/MD), serve with Ollama + LangChain/LlamaIndex, add citations
- Agent with 5 Tools: Web search, code execution, file ops, API caller, database query — orchestrate with LangGraph
- Eval Harness: 50 test cases, automated regression on every prompt/model change
Don't take courses. Build things that break, then fix them. The portfolio proves competence; the certificate proves you watched videos.
Phase 2: Production Patterns (Month 3–4)
Self-Hosted Model Serving
Cloud APIs are fine for prototypes. Production needs control:
- vLLM for high-throughput OpenAI-compatible serving (PagedAttention, continuous batching)
- Ollama for local dev + edge deployment (model library, simple API)
- TGI (Text Generation Inference) for enterprise features (quantization, sharding)
Deploy on Kubernetes with KServe / KubeRay for autoscaling, GPU sharing, multi-model serving.
Observability for AI Systems
# Key metrics every AI system needs:
- Request latency (p50, p95, p99)
- Token throughput (input/output tokens/sec)
- Cost per 1K tokens (model + infra)
- Error rate by type (timeout, validation, hallucination)
- Eval score drift (weekly automated eval runs)
- User satisfaction (thumbs up/down, implicit signals)Tools: Langfuse, LangSmith, Arize, Phoenix — or build custom with OpenTelemetry + Grafana.
Multi-Tenancy & Security
- Row-level security for tenant data in vector stores
- Prompt injection defense: system prompt isolation, input sanitization, output validation
- Audit logging every agent action (who, what tool, what data, result)
Phase 3: Portfolio & Positioning (Month 5–6)
The 3 Portfolio Pieces That Get Interviews
| Project | What It Proves | Tech Stack |
|---|---|---|
| Production RAG System | End-to-end: ingestion → embedding → retrieval → generation → eval → monitoring | FastAPI, pgvector, vLLM, LangGraph, Prometheus |
| Multi-Agent Workflow | Orchestration, state management, human-in-loop, error recovery | LangGraph, Temporal, PostgreSQL, React frontend |
| Agent Platform SDK | Developer experience, extensibility, multi-tenancy, auth | TypeScript, Python, OpenAPI, CLI, docs |
Positioning: "AI Solutions Architect" not "Prompt Engineer"
Title matters. Frame your DevOps background as reliability engineering for AI systems:
- "I build production-grade AI agent infrastructure"
- "I design eval-driven development workflows for LLM apps"
- "I operate self-hosted model serving at scale"
2026 Salary Benchmarks (North America, Remote-Friendly)
| Role | Base Range | Total Comp (with equity) |
|---|---|---|
| Senior DevOps Engineer | $160–200K | $200–280K |
| AI/ML Platform Engineer | $180–230K | $240–350K |
| AI Solutions Architect | $200–260K | $280–400K+ |
| Staff AI Platform Engineer | $250–320K | $350–500K+ |
| Independent Consultant (AI Automation) | $300–500K+ | Variable (project-based) |
Source: Levels.fyi, Wellfound, personal network data points (2025–2026). Consulting rates: $200–500/hr for implementation; $5–15K/month retainers for managed services.
Recommended Learning Resources (Curated, Not Exhaustive)
- LLM Fundamentals: Andrej Karpathy's "Zero to Hero" (YouTube), "Attention Is All You Need" paper
- RAG Deep Dive: Pinecone/LangChain RAG guides, "Retrieval-Augmented Generation" survey paper
- Agents: LangGraph docs, "Reflexion" paper, "Generative Agents" (Stanford)
- Eval: "LM-Eval Harness", "G-Eval", "RAGAS" papers
- Serving: vLLM docs, "PagedAttention" paper, KServe tutorials
Your 30-Day Sprint Plan
| Week | Focus | Deliverable |
|---|---|---|
| 1 | LLM basics + local Ollama + first RAG | Working chatbot over your resume/docs |
| 2 | Agent with 5 tools + LangGraph | Automated research agent (search → summarize → save) |
| 3 | Eval harness + CI integration | GitHub Action runs evals on every push |
| 4 | Deploy to K8s (kind/k3s) + observability | Live endpoint with Grafana dashboard |
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