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SKC Engineering

Software Engineer (AI/ML) Intern

Built WeldCost on FastAPI and LangGraph. Cut p99 latency from 1.2s to 360ms and automated 90% of welding calculations.

Overview

Estimating a weld job is a conversation. You gather parameters one piece at a time, and a naive LLM either forgets what you told it or drifts between runs. WeldCost is built to be dependable. It turns that back-and-forth into a consistent, auditable estimate that engineers no longer do by hand.

What happened

WeldCost: AI-Powered Cost Estimation Platform

The backend is FastAPI with JWT auth over PostgreSQL. The biggest win was caching. Redis in front of the hot read paths took p99 latency from 1.2s to 360ms. That is the difference between an engineer waiting on the tool and an engineer just using it.

The estimation runs as LangGraph workflows with RAG over the welding standards, deployed on Fly Machines. It automates about 90% of the welding calculations that used to be done by hand, which gives the team back 12+ hours a week. The design goal was trust, not novelty. It routes on intent, runs its tools deterministically, and keeps state explicit, so the same inputs produce the same estimate and every output can be traced.

Long sessions get expensive because the context keeps growing. I kept input size flat with rolling context windows, automatic summarization, and state-first prompting, which cut inference cost per request by about 20%. To stop the model changing behavior between deployments, I built a Pytest and LangSmith suite across 60+ cases with human-in-the-loop interrupts and a regression monitor. That cut workflow regressions by 75% and made weekly releases routine.