Skills & Stack
What I Know
& How Well
Skills aren't binary — they exist on a spectrum. Here's an honest map of where I am, what I've mastered, and what I'm actively building toward. No inflated self-assessments.
Mastered
Proficient
Learning
Exploring
Technology Radar
Full Stack
Aa
Mastered — daily production use
Aa
Proficient — regular use
Aa
Exploring — learning or occasional
Skill Map
Where I Stand
● Mastered
Django Ecosystem
Models, ORM, migrations, DRF serializers, ViewSets, signals, middleware, auth, admin customization. Celery for async task queues — basics through Canvas primitives (chain, chord, group). Used daily in production.
● Mastered
PostgreSQL & SQL
Schema design, normalization, indexes, JOINs, transactions, stored procedures, and DBMS concepts (ACID, isolation levels, query planning) — completed at IIT Mandi. Applied in production: 40% latency reduction via ORM optimization.
● Mastered
Python & DSA
Python fundamentals completed at IIT Mandi — data types, OOP, generators, decorators, async. DSA module complete: Arrays, Hashing, Two Pointers, Sliding Window, Stacks, Queues, Binary Search, Linked Lists, Trees. NeetCode 150 ongoing.
● Proficient
FastAPI + Async
Building async REST APIs, dependency injection, Pydantic schemas, background tasks. Used alongside Django for lightweight service endpoints. Planning FastAPI-based AI backends.
● Proficient
Networking Fundamentals
TCP/IP, HTTP/HTTPS, DNS, OSI model, subnetting, REST principles, WebSockets, load balancing concepts — studied at IIT Mandi (module nearly complete). Applied directly in API design and production infrastructure decisions.
● Proficient
Docker & DevOps
Docker, docker-compose, multi-service orchestration (5-service setup in FlowBoard), GitHub Actions CI/CD, Railway CLI for remote deployments.
▲ Learning
AI / GenAI Integration
Integrating OpenAI and Gemini APIs into Django backends. Exploring RAG architecture — pgvector, embeddings, and semantic search. IIT Mandi GenAI module upcoming. Planning production NLP Q&A feature.
▲ Learning
System Design
CAP theorem, consistency models, scalability patterns, latency/throughput trade-offs, CDN strategies, DB sharding. Studying daily — IIT Mandi System Design module upcoming.
◆ Exploring
ML & scikit-learn
ML fundamentals, supervised learning, feature engineering, scikit-learn pipelines. Building theoretical foundation through IIT Mandi GenAI module.
Toolbox
Daily Tools
Python
Django
FastAPI
PostgreSQL
Docker
React
Redis
Celery
Git
Railway
Cloudinary
OpenAI
MacBook Air
SEO Tools
VS Code
GitHub
Postman
IIT Mandi