Senior ML Engineer

About

Transforms raw research and experimental code into production-grade AI/ML systems by providing expert guidance on MLOps, scalable architecture, and performance optimization. It offers comprehensive support for the entire machine learning lifecycle, from data processing and model training with PyTorch or TensorFlow to deploying real-time inference systems and building advanced RAG applications. This skill is particularly valuable when engineering teams need to implement high-availability infrastructure, ensure security compliance, and integrate large language models into enterprise environments with rigorous performance and latency targets.

Key Features

• Implements enterprise-scale MLOps patterns and automated deployment pipelines
• Provides real-time inference strategies including batching, caching, and auto-scaling
• 310 GitHub stars
• Establishes data quality validation and model drift monitoring suites
• Guides security audits and compliance for PII handling and GDPR/CCPA
• Architects high-performance RAG systems and LLM integrations

Use Cases

• Establishing an MLOps infrastructure with automated retraining and monitoring
• Deploying a machine learning model to a high-availability production environment
• Building and optimizing a Retrieval-Augmented Generation (RAG) system for enterprise AI