AK

Aswin Kumar

B.Tech Computer Engineering student · Full-Stack Developer Intern

Panvel, Maharashtra, India

@aswin_kumar

0 followers

🎓 B.Tech in Computer Engineering at Pillai College of Engineering · Graduating 2027

About

Computer Engineering undergraduate focused on backend systems, AI/ML, and real-time data pipelines. Experience building production-style applications and open-source contributions across Python, TypeScript, FastAPI, Kafka, and cloud-native tooling.

Experience

  • Full-Stack Developer Intern
    CloudRion Technologies
    May 2026 – Oct 2026

    Architected a logistics simulation system with route engine and 15s-interval automatic shipment movement. Designed scalable shipment tracking architecture with localStorage and migration logic, developed Leaflet-based interactive map visualization, and solved state management complexities through service-oriented architecture.

Education

  • B.Tech in Computer Engineering
    Pillai College of Engineering
    Computer Engineering · Jul 2023 – May 2027

Skills

1 to 2 years 1 to 2 years A year or two of steady use.
6 to 12 months 6 to 12 months Most of a year behind them.

Tools / apps / platforms

<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
Redis Docker GitHub SQLite Vercel MongoDB Kubernetes PostgreSQL Apache Kafka Hugging Face Elasticsearch Google Cloud Platform Amazon Web Services AWS

Projects

  • F1 Telemetry Anomaly Engine
    Python, FastAPI, Kafka, PyTorch, SQLAlchemy, Next.js, PostgreSQL, NumPy/Pandas

    Production-style telemetry anomaly platform with async inference, streaming, Redis-backed state, and explainable anomaly detection.

  • NightWatch Atlas – Real-Time Intelligence Monitoring Platform
    Kafka, FastAPI, PostgreSQL, WebSockets, React, Docker

    Event-driven platform across six containerized microservices with real-time APIs, hybrid storage, and alerting on AWS EC2.

  • Research Reasoning Engine
    Python, FAISS, NetworkX, NLP Pipelines

    End-to-end RAG pipeline using open-source LLMs and HuggingFace Transformers to extract evidence-backed causal graphs from unstructured text.

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