Aaliyah D.

Key Skills

Software

Other

Top Subject Matter

Agentic conversational AI

NLP annotation frameworks

Dialogue systems training data preparation and NLP labeling

Top Data Types

Text

Document

Image

Top Task Types

Entity (NER) Classification

Question Answering

Classification

Freelancer Overview

R&D Research Lead : Amelia (Agentic AI). Brings 15+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Doctor of Philosophy, Liberty University (2021) and Master of Science, Birla Institute of Technology and Science, Pilani. AI-training focus includes data types such as Text and Document and labeling workflows including Entity (NER) Classification, Question Answering, and Classification.

Labeling Experience

R&D Research Lead - Amelia

TextClassification

Led India's agentic AI research and development for Amelia, an enterprise conversational agent. Managed research initiatives and built systems combining natural language understanding, generative response generation, and heuristic reasoning. Oversaw development of internal annotation and automation frameworks that supported data generation and operational workflows. • Researched and productionized agent versions 2 through 4 • Led NLU, generative, and heuristic responder components • Built an agent dashboard and annotation/data workflows • Developed conversational NLP and reasoning capabilities

2018 - 2021

R&D Research Lead : Amelia (Agentic AI)

TextEntity (NER) Classification

Led an agentic AI R&D effort that included building and maintaining an annotation framework for conversational data. Created AI-driven data generation workflows using LM-based paraphrasers and supported downstream NLP labeling for multiple conversational tasks. The system design focused on producing labeled datasets and operationalizing annotation pipelines for agent responses. • Administered an annotation framework/dashboard for gathering annotations • Generated data via LM-based paraphrasers to expand labeled corpora • Enabled NLP labeling for intent classification, emotion/sentiment, and entity tagging • Supported knowledge base and predicate-logic query work using labeled conversational content

2018 - 2021

Research and Development Engineer - Amelia

TextClassification

Developed deep learning dialogue agent and question answering systems for the cognitive agent AMELIA. Worked on NLP modeling including coreference resolution, question classification, and structured prediction for tagging. Implemented QA-oriented methods such as answer extraction and paraphrasing to improve conversational performance. • Built dialogue and QA components using NLP and ML • Developed classifiers and sequence tagging approaches • Implemented QA paraphasing and short answer extraction • Engineered models using SVMs, CRFs, and related methods

2015 - 2018

Research and Development Engineer

TextQuestion Answering

Developed deep learning dialogue agents and a question answering system that required supervised training data and structured annotations. Implemented components for question classifiers, conditional random field tagging, and QA paraphrasing to support labeled datasets and training examples. The work emphasized transforming raw text into structured training inputs for dialogue and QA models. • Built question classifiers and SVM pipelines using specialized syntactic representations • Used CRF-based tagging approaches to improve text labeling quality • Performed QA paraphrasing and short-answer extraction for training data preparation • Developed systems for dialogue context modeling to support retrieval training data

2015 - 2018

Research Intern - Complex Trait Lab

OtherDocumentClassification

Conducted data science and bio-statistics experiments that involved constructing and evaluating statistical models for labeled biological measurements. Processed biological datasets using statistical testing and regression methods to support analysis of phenotypic patterns. While not an annotation role, the work involved preparing structured analytical outputs used for model training and evaluation. • Tested hypotheses using F-test and Brown–Forsythe statistics • Built regression models including Deming and bivariate regression • Studied differences in phenotypic patterns across conditions • Performed parallel processing on 11,000+ biological markers using R and Python

2014 - 2014

Education

Work History