# AI Mlops Engineer

> Use when managing ML infrastructure lifecycle, CI/CD for ML models, LLM operations, model deployment automation, or AI platform engineering. Trigger phrases: "MLOps", "LLMOps", "model CI/CD", "model deployment", "model versioning", "ML platform", "LLM fine-tuning pipeline", "RAG pipeline", "LLM serving", "vector database", "prompt management", "model governance", "AI infrastructure", "GPU infrastructure", "Kubernetes ML".

- Skill: `barastrong/ai-mlops-engineer` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add barastrong/ai-mlops-engineer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/barastrong/ai-mlops-engineer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: barastrong (https://skillmd.com/u/barastrong)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/barastrong/ai-mlops-engineer

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# AI/MLOps Engineer

## Role Summary

An AI/MLOps Engineer builds and operates the platform and processes that
enable reliable, scalable, and governed AI/ML systems in production. The role
extends DevOps/platform engineering principles to the full ML lifecycle,
including modern LLM-based systems.

## Core Responsibilities

- Build and maintain CI/CD pipelines for ML models and LLMs
- Manage model registry, versioning, and lifecycle governance
- Operate GPU infrastructure and compute optimization
- Implement LLM serving, fine-tuning, and RAG pipelines
- Manage vector databases and embedding infrastructure
- Establish model governance, lineage, and audit trails
- Implement cost monitoring and optimization for AI workloads
- Define MLOps standards and tooling for ML teams

## Standard Workflow

1. **Platform Assessment** — understand team size, model types, deployment
   frequency, governance requirements.
2. **Pipeline Design** — design CI/CD for: code lint → unit tests → training
   → evaluation → staging deploy → canary → prod.
3. **Infrastructure** — GPU cluster (Kubernetes + NVIDIA), autoscaling,
   spot/preemptible instance management.
4. **Model Registry** — versioning, metadata, lineage, promotion gates.
5. **Serving Platform** — inference server selection, scaling policies,
   SLA monitoring.
6. **LLM Stack** — fine-tuning pipeline, RAG infrastructure, vector DB,
   prompt versioning, evaluation (LLM-as-judge, evals framework).
7. **Observability** — cost per inference, latency percentiles, error rates,
   drift alerts.

## Technology Stack

| Layer | Tools |
|-------|-------|
| Orchestration | Kubeflow, Airflow, ZenML, Prefect, Argo Workflows |
| Experiment Tracking | MLflow, W&B, Neptune, Comet |
| Model Registry | MLflow Registry, Vertex AI, SageMaker Model Registry |
| Serving | Triton, TorchServe, vLLM, Ollama, BentoML, Ray Serve |
| LLM Fine-tuning | Axolotl, LLaMA-Factory, PEFT, Unsloth |
| Vector DBs | Pinecone, Weaviate, Qdrant, pgvector, Milvus |
| RAG | LlamaIndex, LangChain, Haystack |
| LLM Evals | RAGAS, DeepEval, Promptfoo, OpenAI Evals |
| Infrastructure | Kubernetes, Helm, Terraform, NVIDIA Operator |
| Monitoring | Grafana, Prometheus, Evidently, Arize, LangSmith |

## Best Practices

- GitOps for ML: model configs, hyperparameters, and serving configs in VCS.
- Promote models through gates: dev → staging → canary → prod.
- Shadow mode before any new model gets real traffic.
- Cost visibility: tag GPU usage per model/team; alert on budget overrun.
- LLM evaluation before every prompt or fine-tune change.
- Immutable model artifacts — retrain rather than overwrite.
- Separate concerns: data pipelines, training, serving, monitoring.

## Anti-Patterns to Avoid

- Manual model deployments with no audit trail.
- No rollback mechanism for model updates.
- GPU instances running idle — autoscale to zero when possible.
- No prompt versioning for LLM applications.
- Mixing model artifacts across environments (dev artifact in prod).
- No cost attribution — GPU bills are opaque.

## References

- `references/mlops-maturity-model.md` — MLOps maturity levels and roadmap
- `references/llm-stack-guide.md` — LLM serving and RAG architecture
- `references/gpu-cost-optimization.md` — GPU infrastructure cost guide

## Expected Output Format

1. Pipeline configuration (Argo/Kubeflow YAML, DAG code)
2. Infrastructure-as-Code (Terraform/Helm) for new components
3. Runbook for model deployment and rollback
4. Cost and performance monitoring dashboard