Pal Nakrani

AI/ML engineer. I build LLM, RAG, and NLP systems that assume the input is hostile and the model will be wrong.

About

I build production AI systems, from the model and LLM pipelines to the backend and frontend around them.

AI/ML engineer, 3+ years on production LLM, RAG and NLP systems. I build assuming the input is hostile and the model will be wrong, so most of my effort goes into the checks that catch it.

Until September 2026, Lead AI engineer at Kyoorious.ai, on LLM evaluation and semantic deduplication over vector embeddings. Comfortable across the stack, model to frontend.

Based in Surat, India, working remotely.

Selected Work

01
LeadTriage: AI Lead Qualification Agent

Scores inbound leads and drafts the reply. Nothing sends without a human.

Injection demanding a score of 100 was scored 0

Next.jsTrigger.devGeminiMongoDBAI Agents
Read case study →
Live · 2026
02
DocSentry: Document Q&A with Guardrails

Answers only from your documents, cites the exact section, or refuses.

0.943 context recall, measured over 123 labelled questions

RAGRAG EvaluationRAGASLangChainFastAPIpgvectorLangSmith
Read case study →
Live · 2026
03
InvoiceLens: Invoice Extraction with Validation

Reads an invoice, then checks the model's work before it reaches a ledger.

7 deterministic checks, 51-case validation suite

Document AIFastAPIGeminiSQLiteReact
Read case study →
Live · 2026
04
TriageDeck: AI Message Intake and Triage

Classifies inbound messages and escalates by rule, not by tone.

A message demanding urgency 100 came out at urgency 0

AI ClassificationFastAPIGeminiSlackReact
Read case study →
Live · 2026

Also shipped

LLM Answer-Evaluation Service

2025

An LLM service that grades free-text student answers and generates feedback, replacing manual grading at scale at Kyoorious.ai.

PythongRPCLLM EvaluationPrompt Engineering

Question-Deduplication Pipeline

2025

A two-stage pipeline combining lexical trigram matching with semantic search over pgvector to keep large question banks free of near-duplicates.

pgvectorEmbeddingsSemantic SearchPostgreSQL

BERT Email Classification

2023

A multi-class email classifier fine-tuned from pre-trained BERT, reaching roughly 90% accuracy with preprocessing tuned for large datasets.

BERTNLPScikit-learnPython

Experience

Feb 2025 - Sep 2026

Lead AI Engineer

Kyoorious.ai

Automated assessment, curriculum and reporting. LLM evaluation and semantic search over vector embeddings.

Jul 2024 - Feb 2025

Associate AI/ML Developer

Workdesk Technologies

RAG models, sentiment analysis, and web-scraping automation for clients.

Dec 2023 - Jun 2024

Associate AI Intern

Logictrix Infotech

Conversational AI chatbots, CV video personalization, translation backends.

May 2023 - Jul 2023

Machine Learning Intern

Logictrix Infotech

BERT email classification (~90% accuracy) and data preprocessing at scale.

Education

2021 - 2024

B.Tech, Artificial Intelligence & Data Science

Uka Tarsadia University

CGPA 7.79 / 10

2018 - 2021

Diploma, Computer Engineering

Uka Tarsadia University

CGPA 9.44 / 10

Stack

Languages

Python, TypeScript, JavaScript, SQL

AI / ML

LLMs, Generative AI, RAG, LLM Evaluation, Prompt Engineering, Prompt Injection Defense, NLP, Semantic Search & Embeddings, BERT, Scikit-learn

LLM Tooling

LangChain (LCEL), LangSmith tracing, Gemini API, OpenAI API, Anthropic API, Claude Agent SDK, structured outputs, ChromaDB, pgvector

Backend

FastAPI, Flask, Node.js, Express.js, gRPC, REST APIs, background workers, Trigger.dev

Data & Infra

PostgreSQL, pgvector, MongoDB, ChromaDB, SQLite, Docker, AWS S3, Git / GitHub

Contact

Let'stalk.
Location
Surat, India · IST (UTC+5:30)
Hours
Evenings and late nights, overlapping US and UK
Open to new workFull-time, remoteContractFreelance

I keep evening and late-night slots open for US and UK hours, so book whatever suits your day rather than mine.

Send a message

I usually reply within a day.

Book a call

These are my real open slots, straight off my calendar and shown in your timezone. Twenty to thirty minutes is usually enough to work out whether there is something here.