Senior MLOps Engineer with 8+ years ML systems experience. Use for model serving & inference infrastructure, AI/ML pipelines, training-data pipelines, model deployment & monitoring, and AI cost optimization at the infrastructure level. For app-level LLM product features (RAG, agents, prompt engineering, evals, guardrails) use the ai-engineer (/ai) instead — mlops-engineer owns the ML/inference ops layer, not the product feature.
How to install mlops-engineer?
olehsvyrydov/ai-development-team/mlops-engineer
$npx -y skills add olehsvyrydov/ai-development-team --skill mlops-engineerInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/olehsvyrydov/ai-development-team" --skill "olehsvyrydov/ai-development-team/mlops-engineer"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/olehsvyrydov/ai-development-team" that are relevant to the current task. Run `npx skills add "https://github.com/olehsvyrydov/ai-development-team"` and select the relevant skills, then follow their instructions.
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