About
Independent AI engineer working on the production reliability of LLM applications.
I work with founders and engineering teams shipping LLM-powered products into production. The focus is on reliability — the engineering between an impressive demo and a system you can put in front of real users.
Services
Evaluation systems
Custom eval harnesses, error analysis workflows, and LLM-as-judge scoring calibrated against human labels. The discipline that lets you ship confidently.
Retrieval (RAG)
Hybrid search, rerankers, query routing, and synthetic eval data. Retrieval that performs on inputs you have not seen yet.
Agent workflows
Stateful graphs and state machines for long-running tasks. Context engineering that keeps agents from drifting or looping.
Production engineering
Prompt caching, model routing, structured outputs, observability, and cost-per-successful-completion tracking. The invisible work that holds production together.
How I work
- Evaluations come first. If we cannot measure improvement, we are guessing.
- Reliability is the product. Cost is a downstream consequence of doing reliability well.
- Direct API calls before frameworks. Reach for LangGraph, etc., when there is a clear reason.
- Outcomes documented in numbers from your eval suite, not vibes.
Engagements are typically scoped over four to twelve weeks, with deliverables documented in the eval suite. Code, documentation, and the harness transfer to your team.
Contact
The most reliable way to reach me is email.
work.shivamsharma@zohomail.in →