Pachaiappan

I'm

About

I am a dedicated AI Engineer with a strong foundation in machine learning and software development. With a passion for creating innovative solutions, I excel in building and optimizing AI-driven applications that address complex challenges. My experience spans various aspects of the AI lifecycle, from model development to deployment, always aiming to deliver high-impact results. I thrive in dynamic environments where I can continuously learn and contribute to cutting-edge projects that push the boundaries of technology.

Birthday: 11 February 2003
Phone: +91 6380344040

City: Puducherry
Email: pachaiappan1102@gmail.com

Interests

Software Development

Machine Learning

Computer Vision

Natural Language Processing

Software Engineering

Visualization

Algorithms

Image Processing

Education

Bachelor of Commerce with Computer Applications

September 2020 - May 2023

Relevant Coursework

Tally Software
Advance Excel

Online Certification

Data Analytics Capstone

Data Structures + Algorithms

Python for Data Science, AI and Development

ML & DS Bootcamp

Data Pre-Processing

Python pro Bootcamp

Experience

Code Clause Intenship

March 2024

DATA SCIENCE INTERN

During my internship, I focused on developing the Crop Disease Classifier project, aimed at classifying diseases affecting major crops in Bangladesh.
This involved leveraging datasets from PlantVillage, Rice Disease, and Wheat Disease to train and evaluate the model.
The ultimate goal was to provide farmers with a tool for early disease detection, thereby contributing to the advancement of agricultural technology and empowering farming communities.

AquilaX.AI based on London

July 2024 - Present

AI/ML ENGINEER

Engineered and launched SecuriTron AI, our flagship AI-driven AppSec assistant trained on 300 M+ open-source projects integrating eight industry-standard SAST scanners with bespoke Qwen3 and LLaMA models to produce structured JSON vulnerability reports at 96% accuracy and reduce false positives by 20%.
Designed, optimized, and released under Apache 2.0 a high-performance ML model that powers a core AquilaX portal UI feature achieving sub-100 ms inference latency, enabling real-time vulnerability triage, and driving community engagement through open-source contributions.
Architected and maintained the FastAPI backend and Streamlit/React frontend to orchestrate scan workflows, enable real-time result visualization, and support 100+ concurrent users with high availability.
Implemented end-to-end CI/CD pipelines using GitHub Actions and Docker Compose, and integrated MLflow with Weights & Biases for automated testing, deployment, and experiment tracking accelerating release cycles by 30%.