Experience
Software Development Engineer
Qburst Technologies
2023 — Present
Building and maintaining scalable backend systems across real estate, travel & hospitality, and GenAI platforms.
Backend systems • Python • Django REST Framework
Ashik M — API & System Architecture Engineer
I design backend systems that stay fast and correct under real load — concurrent bidding engines, caching layers that cut redundant calls, and RAG pipelines that get measured, not just shipped. ~3 years across real estate, travel, and GenAI platforms.
$
$
$
$
Background
Where this experience comes from.
Education
B.Tech, Computer Science
LBS College of Engineering
2019 — 2023
Affiliated with APJ Abdul Kalam Technological University, Kerala. Final year project on CNN-based classification of pneumonic chest X-rays.
Core Expertise
Systems built to hold up under concurrency, load, and change.
Real-Time Concurrency
Built a live bidding engine that resolves simultaneous offers correctly under pressure, with no lost or double-counted bids.
Caching & Performance
Redesigned caching strategy across services, lifting hit rates and cutting redundant third-party API calls at the source.
Distributed Task Processing
Coordinated Celery, Celery Beat, and RabbitMQ to move heavy work off the request path without losing reliability.
Database & API Reliability
Maintained DRF microservices carrying financial calculations, where accuracy and consistency aren't optional.
GenAI & RAG Pipelines
Built retrieval pipelines on LangChain, Pinecone, and ChromaDB, then measured them with RAGAS instead of trusting them blindly.
Cloud & DevOps
Ran production workloads on Docker and AWS (EC2, S3, Lambda), tuning Gunicorn workers to keep file-heavy endpoints stable.
Featured Projects
A closer look at what shipped, and what it changed.
Real-Time Auction Platform
- Handled concurrent bidding at scale with resilient event flow.
- Tuned Gunicorn workers for reliable file handling under load.
- Integrated DocuSign for in-app e-signing workflows.
Travel & Hospitality Backend
- Improved API response times by tracking down backend bottlenecks.
- Cut redundant third-party API calls through redesigned caching.
- Raised cache hit rates across distributed workflows.
Generative AI Document Q&A
- Built an end-to-end RAG pipeline for intelligent document Q&A.
- Evaluated pipeline quality with RAGAS: faithfulness, relevancy, context recall.
- Managed embedding generation and vector retrieval across collections.
Tech Stack
PythonDjangoDjango REST FrameworkPostgreSQLMongoDBRedisRabbitMQCeleryDockerAWS (EC2/S3/Lambda)LangChainPinecone
PythonDjangoDjango REST FrameworkPostgreSQLMongoDBRedisRabbitMQCeleryDockerAWS (EC2/S3/Lambda)LangChainPinecone
Get In Touch