Building production systems that solve real problems
I design and build scalable backend systems, from AI-powered platforms to complex infrastructure. I think deeply about architecture, trade-offs, and what it takes to ship systems that work in production.
Featured Projects
Production systems solving real-world problems
Camp View
3D campus navigation with real-time pathfinding
FireVision
Real-time fire detection using computer vision
Tectovia Quiz
AI-powered adaptive quiz generation platform
GASC Sync
Unified college management platform
Experience
Building production systems that scale
Backend and Machine Learning Intern
Sep 2025 - Dec 2025// Problem Statement
Building backend systems for an AI-driven medical triage platform
// Technical Contributions
- ▹Developed backend APIs for patient and doctor workflows, including authentication, report submission, and triage history retrieval
- ▹Integrated a pre-trained AI inference service via REST APIs to process patient inputs and generate medical triage reports
- ▹Designed role-based access control for patients and doctors to securely view reports and medical data
- ▹Worked with MongoDB schemas and API validation to ensure reliable data flow across the system
// Impact: Enabled a functional end-to-end medical triage workflow used for internal product validation
Founder & Backend Engineer
Jan 2025 - June 2025// Problem Statement
Building scalable backend systems for campus-scale navigation and AI-driven applications
// Technical Contributions
- ▹Designed and implemented backend services for a 3D campus navigation platform using GeoJSON-based map data
- ▹Built REST APIs for route computation, location data access, and system configuration
- ▹Handled database design, API architecture, and deployment on cloud infrastructure
- ▹Led technical decision-making and documentation for system design and backend workflows
// Impact: Successfully developed a working prototype and tested in real environments
Machine Learning Intern
July 2024 - Jan 2025// Problem Statement
Applying computer vision models for real-time robotic perception
// Technical Contributions
- ▹Developed computer vision pipelines for object detection and facial landmark tracking
- ▹Worked with real-time video streams to preprocess input data and optimize inference speed
- ▹Collaborated with the engineering team to integrate ML models into robotic control workflows
- ▹Improved model reliability through experimentation, tuning, and validation
// Impact: Contributed to improved perception accuracy and smoother real-time robotic navigation
// System Design
Deep dives into architecture decisions
Scalable Video Processing Pipeline
Process real-time video streams for fire detection with sub-second latency while handling multiple concurrent streams
>> Architecture
- 1.Message queue (Redis) for stream ingestion and load distribution
- 2.Worker pool with auto-scaling based on queue depth
- 3.Optimized ML inference using ONNX runtime and GPU acceleration
- 4.WebSocket server for real-time alert delivery
- 5.PostgreSQL for alert storage with time-series optimization
>> Key Decisions
- ▹Redis over Kafka: Lower latency for real-time processing, simpler ops
- ▹ONNX runtime: 3x faster inference than PyTorch for production
- ▹Worker auto-scaling: Maintain <100ms latency during traffic spikes
>> Trade-offs
- ⚠Memory vs Latency: Keep 3 video frames in memory for temporal analysis
- ⚠Accuracy vs Speed: Optimized model to 95% accuracy for 10x throughput
- ⚠Cost vs Reliability: Redundant workers increase cost but prevent alert drops
>> Scalability
Horizontal scaling of workers handles 100+ concurrent streams. Queue prevents backpressure. Redis Cluster for HA.
Multi-Tenant SaaS Architecture
Build college management system supporting 50+ institutions with data isolation, custom configurations, and shared infrastructure
>> Architecture
- 1.Shared database with tenant_id partitioning for cost efficiency
- 2.Row-level security (RLS) policies for data isolation
- 3.Redis for per-tenant configuration caching
- 4.API gateway with tenant routing and rate limiting
- 5.Background workers for async operations (email, reports)
>> Key Decisions
- ▹Shared DB over database-per-tenant: 10x cost savings, acceptable security with RLS
- ▹PostgreSQL partitioning: Query performance scales linearly with tenant count
- ▹Redis caching: Reduced DB load by 70% for config-heavy operations
>> Trade-offs
- ⚠Isolation vs Cost: Shared DB reduces cost but requires strict RLS policies
- ⚠Flexibility vs Complexity: Custom per-tenant configs increase system complexity
- ⚠Performance vs Consistency: Eventual consistency for non-critical updates
>> Scalability
Partitioned tables scale to 1000+ tenants. Read replicas for analytics. Sharding strategy planned for 10k+ tenants.
Education
Academic foundation and continuous learning
Bachelor of Science (Computer Science)
2023 - 2026Artificial Intelligence & Data Science
// Key Achievements
- ▹Currently pursuing 3rd year with a percentage of 83
- ▹Built multiple full-stack and backend-heavy projects as part of project-based learning
- ▹Worked extensively with REST APIs, databases, authentication, and system design fundamentals
- ▹Collaborated in team projects, handling backend architecture and API design responsibilities
Higher Secondary Education
2021 - 2023Arts (Commerce, Economics, Business Mathematics)
// Key Achievements
- ▹Scored 89%+ in board examinations
Skills
Technologies I work with
// Backend
// Databases
// DevOps & Tooling
// AI / ML
Resume
Detailed overview of my experience, projects, technical skills, and achievements in backend engineering and system design
Get in Touch
Open to discussing backend systems, architecture, and new opportunities
Built with Next.js · Designed for engineers