Lanke Kiran Teja
I'm a Computer Science student who loves building apps that solve real problems—comfortable with Flutter for mobile,
React for web, and
Node.js /
FastAPI on the backend. I focus on readable code and interfaces that feel good to use.
Tech Stack
Featured Projects
Kiden Hub
All-in-one productivity workspace: task management, focus tracking, and personal analytics with a Kanban board, Markdown notes, and offline-first sync to Supabase.
Fillora
Mobile app to automate repetitive form filling with AI-assisted inputs, voice-to-text, multilingual support, and privacy-first local storage with biometrics and PDF export.
Greendot
Farmer’s assistant: crop disease identification from photos, real-time market prices, smart irrigation and harvest reminders, with offline-friendly caching for rural connectivity.
MailX
ML-based email classification into work, personal, and spam using NLP feature extraction and tuned models so important messages surface reliably.
FinScribe
Financial report generator: structured MD&A-style reports from raw financial data via a FastAPI backend and responsive React frontend.
Experience
Full-Stack Developer
LifeMonk•Hyderabad, India
Educational platform with Vite CMS admin, React Expo mobile app, and Xano backend—challenges, courses, and school-scoped data.
- -Built and debugged the platform; fixed critical school-level data isolation and course mapping with per-school templates
- -Designed Challenges across CMS and mobile (course linking, time periods); managed 50+ Xano APIs with entitlement handling
SoAI Hybrid Intern
Swecha•Hyderabad, India
LLM inference in production: scalable APIs, latency optimization, and reliability under load.
- -Deployed inference pipelines from local checkpoints to APIs handling 1000+ req/min
- -Token-level optimizations and attention caching cut latency ~40% under concurrency; monitoring and fallbacks for ~99.5% uptime
AIML Virtual Intern
AICTE Remote•Remote
Supervised learning on multiple datasets with full ML pipelines from preprocessing through evaluation.
- -Tuned models to 85–92% accuracy on classification tasks across five datasets
- -Built end-to-end workflows: preprocessing, feature engineering, training, and evaluation
Networking Development Intern
HCL Tech Bee Scholar•Lucknow, India
Production network diagnostics, QoS improvements, and Python automation for monitoring and logs.
- -Resolved 20+ network bottlenecks; QoS changes improved throughput ~35%
- -Python automation for monitoring and log analysis cut troubleshooting from hours to minutes per incident
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kiranlanke824@gmail.com
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