What is the Senior ML/AI Engineer skill for Claude Code?

It is a specialized capability that provides Claude with the frameworks, architectural patterns, and scripts necessary to build and maintain production-grade machine learning and AI systems.

Which tech stack is covered by this skill?

The skill covers a broad stack including Python, PyTorch, TensorFlow, Spark, Kubernetes, LangChain, and various vector databases like Pinecone.

Does this skill support RAG and LLM fine-tuning?

Yes, it includes specific documentation and implementation patterns for building advanced Retrieval-Augmented Generation systems and fine-tuning large language models for production.

Can it help with MLOps and monitoring?

Absolutely. It provides best practices for model deployment, drift detection, feature stores, and automated retraining pipelines to ensure system reliability.

Is this skill suitable for high-scale enterprise applications?

Yes, it is designed with performance targets like sub-100ms latency and high availability (99.9% uptime) in mind, following enterprise-scale architectural patterns.

Senior ML/AI Engineer

Name: Senior ML/AI Engineer
Author: pur3v4d3r

bypur3v4d3r

•

Data Science & ML

Productionizes machine learning models and builds scalable AI systems with deep expertise in MLOps, RAG architectures, and high-performance inference.

About

This skill equips Claude with the capabilities of a world-class ML engineer, focusing on the transition from experimental models to production-grade AI systems. It provides comprehensive patterns for MLOps, including automated retraining pipelines, drift detection, and feature store integration. Beyond traditional ML, it excels at modern AI challenges such as architecting Retrieval-Augmented Generation (RAG) systems, fine-tuning LLMs, and deploying agentic workflows. By applying these patterns, users can ensure their AI infrastructure is scalable, secure, and optimized for low-latency performance in enterprise environments.

Key Features

Performance optimization for high-throughput, low-latency model inference.
Advanced RAG system architecture design and LLM integration.
End-to-end MLOps and automated model retraining pipeline implementation.
Comprehensive monitoring for model drift, data quality, and system health.
1 GitHub stars
Cloud-native deployment patterns using Kubernetes, Docker, and AWS/GCP.

Use Cases

Establishing MLOps best practices and automated CI/CD for machine learning models.
Optimizing ML inference latency and throughput for high-traffic applications.
Architecting and deploying a production-ready RAG system for enterprise data.

About

Key Features

Performance optimization for high-throughput, low-latency model inference.
Advanced RAG system architecture design and LLM integration.
End-to-end MLOps and automated model retraining pipeline implementation.
Comprehensive monitoring for model drift, data quality, and system health.
1 GitHub stars
Cloud-native deployment patterns using Kubernetes, Docker, and AWS/GCP.

Use Cases

Establishing MLOps best practices and automated CI/CD for machine learning models.
Optimizing ML inference latency and throughput for high-traffic applications.
Architecting and deploying a production-ready RAG system for enterprise data.