I help AI and machine learning teams build reliable infrastructure for experimentation, evaluation, deployment, and monitoring.

I can set up a practical MLOps or LLMOps foundation that helps your team track experiments, version models and datasets, evaluate performance, deploy services, and monitor production behavior.

What I can deliver:

MLflow experiment tracking

Model and prompt versioning

Model registry setup

LLM tracing and evaluation workflows

Dataset validation and quality checks

Reproducible training and inference pipelines

Docker-based model or API deployment

CI/CD workflows for AI applications

Batch and real-time inference services

AWS, Azure, or private-cloud deployment support

Monitoring, logging, and failure alerts

Technical documentation and handover

I work with Python, Docker, MLflow, cloud services, REST APIs, and production-oriented AI architecture. The setup can support traditional machine learning models, LLM applications, RAG systems, fine-tuned models, and multi-agent workflows.

MLflow supports experiment tracking, model evaluation, model registry, deployment, LLM tracing, agent evaluation, and prompt management, making it suitable for both traditional ML and modern LLM applications. mlflow.

I can start with an infrastructure audit and then implement only the components your team actually needs, avoiding unnecessary platform complexity.