Topu Kumar Mondol
I build backend and AI systems in Python — agentic pipelines, retrieval, and the infrastructure that keeps them running.
B.Sc. in Electrical & Computer Engineering, Rajshahi University of Engineering & Technology
Graduating October 2028 · CGPA 3.51 / 4.00 · Rajshahi, Bangladesh
Projects
OpsIQ — AI-powered ops intelligence platform
Python · FastAPI · LangGraph · LangChain · FAISS · PostgreSQL (async SQLAlchemy) · 2026
- Verified root-cause classification accuracy by replaying three documented production incidents — GitLab 2017, Cloudflare 2019, AWS 2020 — through the pipeline, asserting both category matches and domain-specific terms drawn from the published postmortems.
- Built a 5-node LangGraph DAG where log analysis and timeline extraction run in parallel and join at root-cause inference, over three temperature-tuned LLM instances (0.7 chat, 0.3 RAG, 0.1 postmortem) isolating generative, grounded and deterministic tasks.
- Replaced per-request session lookups with stateless HMAC-SHA256 tokens carrying a version field, so bumping one database column invalidates every issued token without the server storing any of them.
- Made restarts transparent by persisting FAISS stores to disk under per-user, per-session paths and mirroring conversation state to PostgreSQL, restoring summary buffers and recent messages on reconnect.
- Automated the release path with a GitHub Actions pipeline that runs the 123-test suite on every push and deploys only when tests pass, replacing manual redeploys.
ContentPlatform — personalized recommendation engine
FastAPI · PostgreSQL · SQLAlchemy 2.0 · JWT + OTP auth · APScheduler · Docker · Pytest · Locust · 2025
- Sustained a 99% success rate at 100 concurrent users (~800ms p95) by load testing with Locust from 10 to 500 users, and root-caused the failure at 500 as database connection pool exhaustion.
- Stopped any one category from dominating the feed by capping it at 3 of the top 5 slots per page, using a slot-based ranker with softmax sampling at temperature 0.7 and 10% random injection.
- Kept recommendations following current behaviour rather than old activity by decaying interaction weights (view 1, like 3, save 5) on a 30-day constant, recomputed for every user in hourly batches.
- Delivered a full 20 posts per page with zero repeats across the entire feed by building all pages in one pass against a shared seen-set, seeded so pagination stays stable.
- Cut admin block latency from up to 7 days to a single request by re-checking user state in the database on every authenticated request instead of trusting the JWT.
Technical skills
- Languages
- Python, C++, C, SQL
- AI and ML
- LangChain, LangGraph, RAG, retrieval systems, vector databases (FAISS), embeddings, LLM integration
- Backend and databases
- FastAPI, SQLAlchemy 2.0, PostgreSQL, REST APIs, JWT auth, APScheduler
- Cloud and infrastructure
- Docker, CI/CD with GitHub Actions, Hugging Face Spaces, Neon managed PostgreSQL, containerized deployment
- Tools
- Git, GitHub, Pytest, Locust, Pydantic
Certifications
Machine Learning Specialization
DeepLearning.AI / Andrew Ng, Coursera · December 2025
CS50x: Introduction to Computer Science
HarvardX, edX · October 2024
CS50P: Introduction to Programming with Python
HarvardX, edX · 2024
Algorithms
Solved 400+ data structures and algorithms problems across LeetCode, GeeksforGeeks and Codeforces, spanning arrays, trees, graphs, binary search and dynamic programming.
Open to Summer 2027 software engineering internships. Reach me at topukumar538@gmail.com.