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senior-ml-engineer

combined

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training. World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.

When to use

• deploying ML models to production
• setting up MLOps infrastructure
• monitoring model performance or drift
• building RAG pipelines
• integrating LLM APIs with retry logic and cost controls
• model serving
• A/B testing ML
• automated retraining

When not to use

• model research
• initial training

Skill page

Conflict

The two copies overlap on MLOps, model deployment, monitoring, and LLM/RAG integration, but each contains material steps the other lacks. The first copy is a broad role/competency guide with performance targets, security/compliance, team leadership, and generic commands, while the second copy provides concrete operational workflows: a step-by-step deployment sequence with validation thresholds, a Docker template, serving options, MLOps pipeline setup with feature store and retraining triggers, LLM provider abstraction with retry/cost management, and RAG implementation with vector DB selection and chunking. Crowning one would hide the other's real alternative content.