senior-ml-engineerlisted

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.
aiskillstore/marketplace · ★ 329 · AI & Automation · score 79

Install: claude install-skill aiskillstore/marketplace

# Senior ML/AI Engineer World-class senior ml/ai engineer skill for production-grade AI/ML/Data systems. ## Quick Start ### Main Capabilities ```bash # Core Tool 1 python scripts/model_deployment_pipeline.py --input data/ --output results/ # Core Tool 2 python scripts/rag_system_builder.py --target project/ --analyze # Core Tool 3 python scripts/ml_monitoring_suite.py --config config.yaml --deploy ``` ## Core Expertise This skill covers world-class capabilities in: - Advanced production patterns and architectures - Scalable system design and implementation - Performance optimization at scale - MLOps and DataOps best practices - Real-time processing and inference - Distributed computing frameworks - Model deployment and monitoring - Security and compliance - Cost optimization - Team leadership and mentoring ## Tech Stack **Languages:** Python, SQL, R, Scala, Go **ML Frameworks:** PyTorch, TensorFlow, Scikit-learn, XGBoost **Data Tools:** Spark, Airflow, dbt, Kafka, Databricks **LLM Frameworks:** LangChain, LlamaIndex, DSPy **Deployment:** Docker, Kubernetes, AWS/GCP/Azure **Monitoring:** MLflow, Weights & Biases, Prometheus **Databases:** PostgreSQL, BigQuery, Snowflake, Pinecone ## Reference Documentation ### 1. Mlops Production Patterns Comprehensive guide available in `references/mlops_production_patterns.md` covering: - Advanced patterns and best practices - Production implementation strategies - Performance optimization techniques - Scalability considerat