Ajay Prakash Nair
Data-focused Software Engineer with 26 months (2 years 2 months) of experience in building scalable search systems, machine learning pipelines, and data-driven applications.
Experience
Software Engineer
Digital Exhibition Group
Milan, Italy
Built and optimized scalable full-stack applications using React, Node.js, and AWS, increasing user engagement by 67% Developed and integrated REST APIs and cloud-based solutions (S3, Lambda, CloudFront) to support concurrent users at scale
IT Trainee
Fidelity National Information Services
Chennai, India
Integrated APIs and improved data handling workflows Enhanced system reliability and reduced bugs
Software Engineer Intern
Traversari Digital Strategies
Venice, Italy
Designed and deployed a scalable search system using Elasticsearch and FastAPI, achieving 95% query accuracy Reduced average search latency by ~2 seconds through semantic search and ranking optimization (BM25, embeddings) Built containerized services using Docker for efficient deployment and scalability
Software Engineer Intern
BMAT Music Innovators
Barcelona, Spain
Migrated legacy frontend from Nuxt2 to Nuxt3, resolving 10+ performance bottlenecks Improved page load speed by 25% and enhanced UI responsiveness using modern Vue.js practices
Education
Universita Ca’ Foscari Venezia
M.Sc.
Major : Artificial Intelligence and Data Engineering (GPA: 85/110)
APJ Abdul Kalam Technological University
B.Tech.
GPA: 8.2/10
Skills
PROJECTS
Enhancing Search in E-Commerce Platforms
Traversari Digital Strategies/ Master’s Thesis Project
Built an advanced search system using Elasticsearch and semantic embeddings, improving relevance and user intent matching Designed scalable FastAPI-based architecture for real-time search performance
Smartphone-based RTI
Personal Project Github
Developed a Python + OpenCV pipeline to generate RTI images from smartphone footage Reduced processing time by 15% using vectorized NumPy operations and multithreading
Analysis & Prediction of Football League
Personal Project Github
Built an end-to-end ML pipeline achieving ~70% prediction accuracy on historical data Automated prediction workflows for future matches
Diabetic Retinopathy Detection
Bachelor’s Thesis Project Github
Developed ML models achieving 85% accuracy on retinal image classification (Kaggle dataset)