senior-ml-engineer

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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.

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Sourcedavila7/claude-code-templates

Added on2026-02-04

NPX Install

npx skill4agent add davila7/claude-code-templates senior-ml-engineer

SKILL.md Content

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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 considerations
Security and compliance
Real-world case studies

2. Llm Integration Guide

Complete workflow documentation in

references/llm_integration_guide.md

including:

Step-by-step processes
Architecture design patterns
Tool integration guides
Performance tuning strategies
Troubleshooting procedures

3. Rag System Architecture

Technical reference guide in

references/rag_system_architecture.md

with:

System design principles
Implementation examples
Configuration best practices
Deployment strategies
Monitoring and observability

Production Patterns

Pattern 1: Scalable Data Processing

Enterprise-scale data processing with distributed computing:

Horizontal scaling architecture
Fault-tolerant design
Real-time and batch processing
Data quality validation
Performance monitoring

Pattern 2: ML Model Deployment

Production ML system with high availability:

Model serving with low latency
A/B testing infrastructure
Feature store integration
Model monitoring and drift detection
Automated retraining pipelines

Pattern 3: Real-Time Inference

High-throughput inference system:

Batching and caching strategies
Load balancing
Auto-scaling
Latency optimization
Cost optimization

Best Practices

Development

Test-driven development
Code reviews and pair programming
Documentation as code
Version control everything
Continuous integration

Production

Monitor everything critical
Automate deployments
Feature flags for releases
Canary deployments
Comprehensive logging

Team Leadership

Mentor junior engineers
Drive technical decisions
Establish coding standards
Foster learning culture
Cross-functional collaboration

Performance Targets

Latency:

P50: < 50ms
P95: < 100ms
P99: < 200ms

Throughput:

Requests/second: > 1000
Concurrent users: > 10,000

Availability:

Uptime: 99.9%
Error rate: < 0.1%

Security & Compliance

Authentication & authorization
Data encryption (at rest & in transit)
PII handling and anonymization
GDPR/CCPA compliance
Regular security audits
Vulnerability management

Common Commands