Data Annotation & AI Training Specialist with IT Support Experience

Rahul Patel

Ph.: 226-***-**** Mail: *****.**********.*****@*****.***

Location: Montreal, QC

WORK SUMMARY:

Adaptable and technically skilled professional with 2+ years of combined experience in Data Annotation and IT Support, with additional hands-on expertise in Python development, machine learning, and web application projects. Proven ability to annotate and evaluate high-quality textual data for AI models at Google AI via Appen, ensuring accuracy in NLP tasks like intent classification, sentiment analysis, and named entity recognition. Formerly provided Tier 1 & 2 IT support for telecom services, demonstrating a strong foundation in incident management, troubleshooting, and end-user training.

Developed and deployed a full-stack Django web application that scrapes and maps fire incident alerts from social media using Facebook Graph and Google Maps APIs. Also conducted exploratory data analysis (EDA) on a Kaggle flower dataset using modern ML tools and techniques. Highly motivated to improve AI, automation that fulfil real-world applications.

SKILL SET:

Data Annotation & AI Training: Label Studio, Appen Platform

Language: Gujarati, Hindi, English

Programming Languages: Python (for data filtering and preprocessing), Excel (QA), JSON

Front-End Technologies: HTML, CSS, JavaScript

Data Access & Database Management: SQL

Version Control & Deployment: Source Control with Git

Development Methodologies: Agile

Ticketing Tools: ServiceNow, Jira

Operating System (OS): Windows, Linux, Mac

MS Tech Stack: Word, Excel, Teams, Outlook

Other Skills: Text Classification, Named Entity Recognition (NER), Sentiment Analysis, Multilingual Data Tagging, Quality Assurance Audits, Annotation Guideline Refinement, Inter-Annotator Agreement (Cohen’s Kappa), Data Cleaning and Label Validation

EXPERIENCE:

Data Annotator /AI Trainer Jan’25 – Present

Google AI via Appen – Contract

Annotated over 100,000 textual data points across English and Hindi datasets to improve Google Assistant’s intent classification and entity recognition accuracy.

Used Label Studio and Appen internal tools to classify intent categories, sentiment labels, and extract named entities for model training.

Reviewed and revised annotation taxonomies in coordination with data science teams, helping reduce annotation ambiguity by 18%.

Participated in cross-checking and QA reviews, maintaining a 98.5% label accuracy across multiple batches of multilingual data.

Analyzed AI-generated outputs post-training to identify labeling inconsistencies and correct edge cases.

Performed annotation audits using Excel filters and Python scripts for data cleansing and validation before submission.

Collaborated with remote teams on dataset versioning, file management, and regular calibration meetings.

Application Support Analyst/IT Support Jul’22 – Dec’23

Gatestone Co. & Inc. (Rogers Communications) Toronto, ON

Provided Tier 1 & Tier 2 technical support for mobile, internet, and TV services, ensuring service uptime and minimal disruption.

Diagnosed and resolved hardware/software issues using knowledge base, resolution trees, and diagnostic tools.

Handled incident management through ServiceNow, tracked escalations, and ensured timely resolution.

Fulfilled service requests such as software installations, account setups, and configuration changes; tracked tasks in JIRA.

Maintained clear documentation of issue-resolution paths and contributed to internal process wikis.

Collaborated with IT infrastructure teams, vendors, and partners to troubleshoot escalated cases.

Delivered user training and onboarding for new employees, promoting tech self-service awareness.

Monitored systems proactively identify early warnings and resolve potential service issues.

Participated in continuous improvement initiatives to reduce ticket volume and optimize resolution flow.

Adhered to IT security standards and compliance protocols throughout all operations.

PROJECTS:

Fire Incident Alert Web App – Python, Django, SQL, Facebook Graph API, Google Maps API

Developed a full-stack web application that scraped Facebook posts from the Windsor-Essex Fire Department page.

Parsed post content to extract incident details and locations, then geocoded locations using Google Maps API.

Displayed incidents as map markers on an interactive Google Maps tab within the app.

Built backend using Django, database with PostgreSQL, and implemented automated scraping logic via scheduled tasks.

Focused on community alerting and location-based incident tracking.

Exploratory Data Analysis on Flower Dataset – Python, Jupyter, Google Colab, Pandas, Seaborn

Conducted full-cycle EDA on a flower classification dataset from Kaggle.

Performed data cleaning, feature scaling, labeling, visualization, and correlation analysis.

Used Pandas, NumPy, Matplotlib, Seaborn for detailed insights and pattern recognition.

Applied machine learning concepts such as feature engineering, label encoding, and distribution analysis.

Environment: Jupyter Notebook, Google Colab; Goal: build data understanding before classification modeling.

EDUCATION:

Masters in Applied Computing (2021-2022)

University of Windsor, Canada – Courses (Introduction to AI, Machine Learning/Pattern Recognition)

Bachelors in Computer Engineering (2015-2019)

Gujarat Technological University, India

CERTIFICATION:

First Look GPT-4 (2025)

Data Cleaning in Python Essentia Training (2023)

Hands-On Data Annotation: Applied Machine Learning (2023)

Artificial Intelligence Foundations (2021)

Python and Django Framework for Beginners (2019) -Udemy

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