For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

Invite to Job
A
Archit K.

Archit K.

AI-Enabled Ticket Triage & Escalation System (LLM workflow with structured labels)

India flagGurgaon, India

Key Skills

Software

No software listed

Top Subject Matter

AI ticket triage
LLM classification
structured parsing for escalation routing

Top Data Types

TextText
DocumentDocument

Top Task Types

Question AnsweringQuestion Answering

Freelancer Overview

AI-Enabled Ticket Triage & Escalation System (LLM workflow with structured labels). Brings 5+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include n8n, OpenRouter, and Supabase. Education includes Bachelor of Engineering, Thapar Institute of Engineering and Technology (2022). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Question Answering, Computer Programming, and Coding.

Labeling Experience

Distributed Order Fulfillment System (automated fulfillment decision workflow)

Developed production-style order fulfillment backend logic with async processing and idempotent workers to support downstream operational decisions. While not a human annotation job, the workflow includes programmatic categorization and rule-based processing that functions as automated labeling for inventory/fulfillment state transitions. The system used Redis and PostgreSQL to coordinate processing and maintain consistent operational records. • Implemented async workers and idempotent processing for reliable state handling. • Used transactional locking and inventory reservation for consistent fulfillment. • Built monitoring via a lightweight Vue.js dashboard for operational oversight. • Persisted and coordinated fulfillment data using PostgreSQL and Redis.

2026

AI-Enabled Ticket Triage & Escalation System (LLM workflow with structured labels)

Question AnsweringQuestion Answering

Built AI-driven ticket triage and escalation workflows using LLM-based classification with structured parsing and persistence. The system used an n8n-driven webhook intake to transform incoming ticket data into labeled categories for automated escalation. Labeled outputs were then stored in PostgreSQL and presented through an internal React intake portal for human-in-the-loop validation and review. • Implemented LLM classification and structured parsing to generate classification labels. • Designed automated escalation logic based on model outputs and extracted fields. • Built persistence and an intake interface to support validation workflows. • Used OpenRouter/Supabase/React tooling to operationalize the labeling pipeline.

2022

Education

T

Thapar Institute of Engineering and Technology

Bachelor of Engineering, Electronics and Communication Engineering

Bachelor of Engineering
2018 - 2022

Work History

A

Accenture

Full Stack Developer

Gurgaon
2022 - Present