KarenTaylor
Backend Engineer
@ Alignerr
I build robust AI-driven software solutions that enhance data processing and analysis capabilities. 🚀
I'm Karen Taylor, a Backend Engineer with a passion for creating end-to-end software solutions. My expertise in Machine Learning and AI allows me to develop systems that effectively process unstructured data and deliver valuable insights. I thrive on transforming complex challenges into efficient, production-ready applications.
About Me
I'm Karen Taylor, a Backend Engineer with a passion for creating end-to-end software solutions. My expertise in Machine Learning and AI allows me to develop systems that effectively process unstructured data and deliver valuable insights. I thrive on transforming complex challenges into efficient, production-ready applications.
CURRENT FOCUS
▹Enhanced model prediction accuracy by 15% through comprehensive analysis of agent responses across various rubrics and state scenarios.
▹Transcribed over 50 audio and video files to generate high-quality training and test datasets.
▹Evaluated LLM conversations to assess the correctness of tool usage, API calls, and reasoning flow.
Projects
tailorcv.ai.tsx
01
AI WEB APP UTILIZING LLMS AND NLP PIPELINES
Tailorcv.ai
- Developed an AI web application that customizes resumes to align with job descriptions, enhancing resume relevance by up to 80%.
- Engineered a Python and FastAPI backend, complemented by HTML, CSS, and JavaScript for the frontend, deployed on AWS.
- Achieved over 50 users within the first week of launch, demonstrating strong early adoption and real-world impact.
AI web app utilizing LLMs and NLP pipelines
youtube-sentiment-analysis.tsx
02
END-TO-END SENTIMENT ANALYSIS PIPELINE
Youtube Sentiment Analysis
- Created a comprehensive YouTube sentiment analysis pipeline processing over 10,000 user comments, enhancing sentiment classification performance through advanced NLP preprocessing techniques.
- Tracked multiple model experiments using MLflow and DVC, facilitating reproducible training and systematic comparison of models developed with scikit-learn and NLP libraries.
- Deployed the pipeline on AWS utilizing Docker, exposing predictions via Flask REST APIs for scalable and reproducible inference.
End-to-end sentiment analysis pipeline
rag-system.tsx
03
PRODUCTION-READY RAG PIPELINE
RAG System
- Developed a production-ready RAG pipeline integrating semantic vector retrieval with LLM generation to produce context-grounded responses.
- Engineered multiple chunking strategies and a scalable ingestion, retrieval, and generation flow for efficient semantic search and generation.
- Implemented history-aware and multimodal augmentations, evaluating retrieval outputs to measure relevance and quality.
Production-ready RAG pipeline
smart-product-pricing-model,-amazon-ml-challenge-2025.tsx
04
NLP AND CV PIPELINE FOR PRICE PREDICTION
Smart Product Pricing Model, Amazon ML Challenge 2025
- Created an NLP and CV pipeline to analyze 150,000 image and text data using transformer-based text encoders and CNN-based image embeddings, integrating them through a fusion neural network for price prediction.
- Implemented data preprocessing techniques, including text cleaning, tokenization, and streaming image feature extraction with ResNet and CLIP representations to manage large datasets.
- Applied feature engineering, outlier handling, and SMAPE-based evaluation to optimize prediction accuracy, achieving a rank of 142 out of 50,000 participants.
NLP and CV pipeline for price prediction
Skills
Natural Language Processing88%
Retrieval Augmented Generation86%
Amazon Web Services (AWS)95%
Data Structures and Algorithms85%
Experience
Jan 2026-Present
Machine Learning Engineer · Alignerr
- Enhanced model prediction accuracy by 15% through comprehensive analysis of agent responses across various rubrics and state scenarios.
- Transcribed over 50 audio and video files to generate high-quality training and test datasets.
- Evaluated LLM conversations to assess the correctness of tool usage, API calls, and reasoning flow.
Education
Jadavpur University
Bachelor of Technology
2023-2027 · Secured 98.5 percentile in JEE Mains out of 1,400,000 students and 1458 rank in WBJEE
Hariyana Vidya Mandir
High School
2020-2022 · Scored 96% in class 10 and 92% in class 12
Leadership
Core Member · Jadavpur University Entrepreneurship Cell
- Organized national-level flagship events such as E-Summit 2025 and Hult Prize 2025, achieving over 5,000 registrations and 1,000+ on-campus attendees.
- Contributed to the establishment of an Incubation Center at Jadavpur University under the Institution’s Innovation Council (IIC).
Coordinator · Jadavpur University Finance Club
- Led planning and execution of Finspire 2025, a national-level finance event with over 1,000 registrations and 500+ on-ground attendees, strengthening the club’s national presence.
- Delivered high-impact trading and investment courses to over 100 students, enhancing engagement in financial markets.
