Jayin P.

Key Skills

Software

No software listed

Top Subject Matter

LLM evaluation and prompt testing for automotive infotainment log root-cause analysis

Software Engineering / Programming (Python) - evaluate code correctness, debug, and judge whether a coding response is right

Artificial Intelligence / Machine Learning - AI explanations, ML reasoning, and technical accuracy on AI topics

Top Data Types

Text

Image

Document

Top Task Types

Text Generation

Evaluation/Rating

Computer Programming/Coding

Prompt + Response Writing (SFT)

RLHF

Text Summarization

Freelancer Overview

Co-Founder & CTO, Lomond.ai — LLM-powered RCA platform (evaluation and prompt testing). Brings 16+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Ollama. Education includes Bachelor of Science, London School of Economics and Political Science (LSE) (2026) and Advanced Level, The Emmbrook School (2022). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Co-Founder & Chief Technology Officer - Lomond.ai

Text

Built an LLM-powered root-cause analysis platform to accelerate diagnostic workflows for automotive infotainment log data. Designed and evaluated model outputs to ensure accuracy, relevance, and consistency throughout the system. Engineered secure, offline-capable local inference pipelines and led end-to-end product delivery from architecture to client deployment. • Developed prompt design and testing workflows for reliable LLM responses • Implemented local LLM inference using Ollama with Electron and automation • Built RSA-based licensing and high-performance data processing pipelines • Managed product lifecycle and supported client deployment activities

2025 - Present

Co-Founder & CTO, Lomond.ai — LLM-powered RCA platform (evaluation and prompt testing)

Text

Developed and iteratively evaluated LLM outputs for a root-cause analysis platform, focusing on reliability of generated responses. Performed continual assessment of LLM outputs for accuracy, relevance, and consistency to improve system performance. Managed the end-to-end evaluation loop to validate model behavior against operational needs.• Engineered prompt design and testing to steer model responses• Reviewed and scored model outputs for correctness and relevance• Maintained consistency checks across generated results• Supported integration of evaluation into offline/local inference pipelines

2025 - Present

Education

Work History