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senior-ml-engineer

Guides production ML deployment, MLOps pipelines, RAG systems, and LLM integration workflows.

2|1|Updated Jan 29, 2026
Search Tags:#mlops#rag#kubernetes#pytorch#model deployment#llm integration#model monitoring
One-click install
npx skills add https://github.com/bhaktofmahakal/ai-counsellor-hf --skill senior-ml-engineer-bhaktofmahakal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-ml-engineer
Source: https://github.com/bhaktofmahakal/ai-counsellor-hf/tree/main/.claude/skills/senior-ml-engineer
Command: npx skills add https://github.com/bhaktofmahakal/ai-counsellor-hf --skill senior-ml-engineer-bhaktofmahakal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Moving machine learning models from notebooks to production requires expertise in deployment pipelines, monitoring, scaling, and LLM integration that most teams lack in one place. ## Core Features & Use Cases - Model Deployment Pipelines: Structure model serving with low-latency inference, A/B testing, feature stores, and automated retraining using Docker, Kubernetes, and cloud platforms. - RAG & LLM Integration: Build retrieval-augmented generation systems and integrate LLMs with LangChain, LlamaIndex, and vector databases like Pinecone. - ML Monitoring & Observability: Set up drift detection, health checks, and performance tracking with MLflow, Weights & Biases, and Prometheus. - Use Case: A team needs to deploy a PyTorch model behind an API with 99.9% uptime, monitor for data drift, and add a RAG-based chatbot—this Skill provides the architecture patterns, reference guides, and scaffolding scripts for each stage. ## Quick Start Ask the AI to design a production deployment pipeline for a PyTorch model with monitoring and drift detection using this skill.

Frequently Asked Questions about senior-ml-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy a PyTorch model to production?▼

Deploy a PyTorch model by containerizing it with Docker, orchestrating with Kubernetes, and adding a serving layer for low-latency inference. The skill's model deployment pipeline script and MLOps reference cover A/B testing, feature stores, and automated retraining.

How to build a RAG system with LangChain?▼

Build a RAG system by combining a vector database like Pinecone with an orchestration framework such as LangChain or LlamaIndex for retrieval and generation. The RAG system architecture reference covers design principles, configuration, and deployment strategies.

What tools are used for ML model monitoring in production?▼

ML model monitoring uses MLflow and Weights & Biases for experiment and model tracking, plus Prometheus for metrics and alerting. The monitoring suite script provides a scaffold for health checks and drift detection workflows.

Does this skill support both TensorFlow and PyTorch?▼

Yes, the skill covers both PyTorch and TensorFlow along with Scikit-learn and XGBoost. Its patterns for deployment, monitoring, and scaling apply across these frameworks.

What are the limitations of the included automation scripts?▼

The scripts in the scripts directory are scaffolding templates with placeholder execution logic rather than complete implementations. They provide CLI structure, logging, and configuration validation that you extend with actual deployment or monitoring code.

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