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Freelance MLOps Consultant

I make AI systems easier to ship, safer to operate, and less painful to maintain once they leave the prototype phase.

Where I create leverage

Production readiness audits for ML and LLM systems
CI/CD, release gates, and operational quality checks
Observability, tracing, and rollback thinking built into delivery

Typical deliverables

Production readiness audit
Delivery workflow and release-gate design
Observability and alerting blueprint
Environment and deployment recommendations
Prioritized hardening backlog

Best fit

Teams moving from notebook or hackathon code to production
Projects where quality is reviewed manually and too late
Organizations needing clearer release discipline around models and prompts

Expected outcomes

More predictable releases

Faster debugging when quality drops

Less hidden operational risk after launch

FAQ

Do you handle classic ML as well as LLM systems?Yes. My work spans forecasting pipelines, experiment tracking, model lifecycle, and GenAI production systems with similar delivery discipline.

What is the first thing you usually fix?Usually the absence of explicit quality gates. Teams often deploy on intuition. I prefer reproducible release criteria and monitoring before scale.

Want to scope this properly?

Send me the use case, current stack, and where the system is failing today. I can usually tell quickly whether the leverage is architecture, evaluation, hardening, or delivery.

Contact me →See more work →