// Melbourne, Australia · open to backend & cloud roles
Kaviraj Ananthapuri
Palanivel
Full-stack engineer, cloud-native — building with AI, grounded in real engineering. React/Next on the front, NestJS & FastAPI microservices behind, shipped on AWS & GCP — plus data engineering and a deepening focus on AI.
01 — About
Full-stack & cloud-native — and going deeper every week.
Full-stack and cloud-native engineer finishing a Master of IT at Monash (GPA 3.875/4.0). I build end-to-end — React/Next on the front, NestJS and FastAPI microservices on the back, deployed across AWS and GCP with Docker, Kubernetes, and IaC. Lately I'm going deep on AI (benchmarking models across hardware, learning the ML foundations) and on the craft itself: system design, low-level design, and the kind of problem-solving that AI tools can't shortcut.
“I care less about chasing every new framework and more about engineering judgement — clean architecture, efficient code, and solving problems that genuinely need experience. AI makes me faster; sharpening my fundamentals makes me valuable.”
Master of Information Technology
Monash University, Melbourne
GPA 3.875/4.0
Expected 2026
Bachelor of Computer Applications
Amrita Vishwa Vidyapeetham
CGPA 9.28/10.0
2020 – 2024
Exchange Semester, Informatics Engineering
Universitat Politècnica de València
2023
LeetCode solved
Master's GPA
Bachelor's CGPA
Currently building & learning
Benchmarking AI models across hardware
Measuring how different models perform on different hardware — throughput, latency, efficiency.
ML foundations, properly
Supervised vs unsupervised learning, classification, and how models actually work under the hood.
Engineering craft
System design, low-level design, and writing efficient code — the depth AI tools can't replace.
Sharper problem-solving
Leaning on AI for speed while building the judgement to solve problems that need real engineering.
02 — Selected work
Things I've built.
From on-device ML and Kubernetes inference services to event-driven microservices. Hover a card.
AI Model Benchmarking
in progressPerformance of AI models across hardware
- ▹Benchmarking how different AI/ML models perform across different hardware — throughput, latency, and efficiency tradeoffs.
- ▹Part of going deeper on the systems side of AI: how models actually run, not just how to call them.
WildQuest
Gamified Wildlife Discovery App
- ▹Android app for discovering, photographing, and logging real animals nearby, with on-device species recognition via a TensorFlow Lite model (MobileNetV2, transfer learning) and a custom FastAPI backend serving location-specific animal data.
- ▹Location-aware quests on Google Maps (custom markers, polylines, live tracking), background step tracking via WorkManager, Firebase auth, and offline persistence with Room.
CloudEco
Marine Plastic Detection Service
- ▹Container-orchestrated ML inference service (FastAPI + YOLOv8m) detecting marine plastic via REST APIs on a highly available GCP Kubernetes cluster provisioned with Terraform and Ansible.
- ▹Sped up inference with OpenVINO INT8 and load-tested using Locust.
Advanced Databases — Data Engineering & EDA
Cleaning, transforming, and finding insight
- ▹End-to-end data work: cleaning, processing, and transforming raw datasets through the full data-engineering pipeline.
- ▹Post-cleaning exploratory data analysis to surface valuable, decision-ready insights for the client.
byteSized
Telehealth Platform — MedHack 2026
- ▹Designed and pitched a telehealth platform with AI-based diet tracking and patient assistants.
- ▹Led product strategy, system design, and high-fidelity prototyping through the pre-implementation phase.
PTV Bus Transport Analysis
Transport Equity, Greater Melbourne
- ▹ogr2ogr ETL pipeline ingesting 10,290+ mesh-block records into PostGIS.
- ▹Assessed transport equity via a service-to-population model, optimised using GIST spatial indexes.
Access Key & Token Services
Web3 Microservices
- ▹Two NestJS microservices for access-key management and Web3 token retrieval over RabbitMQ.
- ▹Key generation, rate limiting, and TTL validation backed by Prisma/PostgreSQL with a request guard enforcing access control.
03 — Toolbox
The stack I reach for.
Languages
Frontend
Backend & Architecture
Cloud & DevOps
Data Engineering & Analytics
AI / ML (actively growing)
Testing & Quality
04 — Experience
Where I've shipped.
Associate Software Engineer Trainee · NeoITO
08/2024 – 11/2024Trivandrum, India
- ▹Built scalable microservices with NestJS and RabbitMQ, implementing event-driven communication between distributed services to improve throughput and reduce API bottlenecks.
- ▹Applied Clean Code principles and Test-Driven Development within an Agile team to improve reliability and maintainability of the codebase.
Teaching & Leadership
Mentoring · clubs · TA (upcoming)
- ▹Ran student clubs during my undergrad — organising, mentoring, and teaching peers.
- ▹In progress: Teaching Assistant role for an upcoming Monash unit.
- ▹I genuinely enjoy explaining hard things simply — teaching is where I do my best thinking.
Certifications
- ✦AWS Certified Cloud Practitioner
- ✦AWS Academy Graduate
- ✦Python for Data Science, AI & Development
Awards
- ★Highest Grade Commendation (Java, Monash FIT)
- ★Excellence in Teamwork
- ★Academic Excellence
05 — Arcade
Stay a while and play.
Four tiny games built around tech and computer science. No high score table — just bragging rights.
Which data structure works on a Last-In, First-Out (LIFO) basis?
06 — Contact
Let's build something reliable.
Open to full-stack, cloud, platform, and AI/ML roles. The fastest way to get a feel for my work? Ask my AI host — or just email me.