senior▌

../../../engineering-team/senior-prompt-engineer/SKILL.md

Production-grade ML engineering expertise for deploying models, building MLOps systems, and scaling AI infrastructure. \n \n Covers model deployment, feature stores, monitoring, and distributed computing with PyTorch, TensorFlow, Spark, and Kubernetes \n Includes LLM integration patterns, RAG system architecture, and fine-tuning workflows using LangChain and LlamaIndex \n Provides production patterns for scalable data processing, real-time inference, A/B testing, and automated retraining pipelin

Expert guidance on prompt optimization, LLM evaluation, and agentic system design for production AI applications. \n \n Covers advanced prompt engineering patterns, few-shot learning, chain-of-thought techniques, and structured output design for Claude, GPT-4, and other LLMs \n Includes RAG optimization frameworks, LLM evaluation methodologies, and performance tuning strategies with reference documentation \n Provides agentic system design principles, multi-agent orchestration patterns, and depl

Automated test suite generation, coverage analysis, and E2E testing for React, Next.js, and Node.js applications. \n \n Three core Python scripts: test suite generator with configurable templates, coverage analyzer with performance metrics and recommendations, and E2E test scaffolder for production-grade automation \n Supports TypeScript, JavaScript, Python across React, Next.js, Node.js, and full modern tech stacks including Docker, Kubernetes, and cloud platforms \n Includes reference document

Automated scaffolding, project analysis, and code quality checks for modern fullstack applications. \n \n Three core Python scripts handle fullstack scaffolding, project analysis with performance metrics, and code quality assessment with automated fixes \n Covers React, Next.js, Node.js, GraphQL, PostgreSQL, Docker, Kubernetes, and major cloud platforms (AWS, GCP, Azure) \n Includes reference documentation for tech stack patterns, architecture design, development workflows, and best practices ac

Complete toolkit for senior secops with modern tools and best practices.

../../../engineering-team/senior-ml-engineer/SKILL.md

../../../engineering-team/senior-secops/SKILL.md

Statistical modeling, experimentation, causal inference, and production ML systems for data-driven decision-making. \n \n Covers experiment design, A/B testing, feature engineering, model evaluation, and causal analysis with Python, R, SQL, and Scala \n Includes production patterns for scalable data processing, ML model deployment, and real-time inference with monitoring and drift detection \n Supports MLOps best practices: automated retraining, feature stores, model serving, canary deployments,

Security engineering tools for threat modeling, vulnerability analysis, secure architecture design, and penetration testing.