Atul K.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Conversational AI (chatbots)

NLP/ML knowledge & intent workflows

AI/ML training coursework (NLP, CV, recommendation)

Top Data Types

Text

Image

Top Task Types

Question Answering

Fine-tuning

Freelancer Overview

Technical Lead — Conversational AI platform and chatbot delivery (Optum Global Solutions/UHG). Brings 19+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Post Graduate Program, University Of Texas - Online (2020) and Bachelor of Technology (Honours), NIT Jamshedpur (2008). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Question Answering and Fine-tuning.

Labeling Experience

Technical Lead — Conversational AI platform and chatbot delivery (Optum Global Solutions/UHG)

Don't discloseTextQuestion Answering

Led delivery of a conversational AI platform by designing and building bot prototypes and conversational components for business use cases. The work included using cloud cognitive capabilities to support intent/knowledge interactions typical of QA and conversational flows. These systems required preparing and validating conversational content and mappings between user utterances and responses. • Designed and built bot prototypes using LUIS and QnA Maker capabilities • Integrated cognitive search and other Azure components into chatbot flows • Developed automated services for operational chat usage reporting consumed by finance teams • Participated in ML architectural discussions for the conversational platform

2018 - Present

Post Graduate Program — Artificial Intelligence and Machine Learning (University of Texas - Online)

OtherFine-tuning

Completed graduate-level coursework focused on training and applying ML models for NLP and related tasks. The program covered end-to-end modeling steps including embeddings, sequential models, and classification for language understanding. It also included computer vision and recommendation systems components relevant to training supervised/unsupervised models. • Natural language processing courses covering BERT/XLNet-style embeddings and classification • Sequential modeling with RNN/LSTM and text classification • Deep learning coursework including CNN-based computer vision training • Recommendation systems coursework using collaborative filtering and model evaluation

2019 - 2020

Education

Work History