Synopsys
LLM evaluation platform
Senior Frontend Developer · Generative AI
Enterprise product UI at Synopsys for benchmarking and comparing large language models, including trial-inference surfaces for Generative AI experiments.
01What I built
Built and maintained the reusable React / Next.js UI for Synopsys’s internal LLM evaluation product: comparison views, trial-inference flows, and the component layer those screens share. Sequenced frontend work by effort versus impact, and used code review and implementation practice to keep TypeScript and UI conventions consistent across the team. Worked with product and design so UX and backlog order matched what stakeholders actually needed to decide.
- Reusable UI for model comparison and trial-inference flows
- Component and styling conventions on Next.js / Tailwind CSS
- Backlog sequencing that weighed engineering cost against decision value
- Frontend standards shared through implementation and review
02Engineering proof
- Reusable Next.js UI for model comparison and trial inference
- Shared frontend standards on an existing product team
- TypeScript delivery path with Docker in the environment
04Problem
Stakeholders needed one product surface to evaluate several LLMs against the same criteria and to run trial inference — instead of disconnected notebooks and one-off demos — before choosing a model for NLP, support automation, content generation, or analysis.
05Scale & constraints
- Frontend ownership of the evaluation product, not model training or weights
- Reusable components had to match an existing design language
- Delivery sat inside an established product team and Docker-based environment
06Outcome
The evaluation product gained reusable frontend surfaces instead of one-off screens, and the team inherited shared UI conventions. Model quality, latency, and selection metrics are not published.
- Reusable UI for an enterprise LLM evaluation product
- Shared frontend standards on the delivery path
07Stack
- React
- Next.js
- JavaScript
- TypeScript
- Tailwind CSS
- Docker
No verified public metrics are published for this engagement.
Categories: AI, Frontend
Year: 2025