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Abhinav0002/README.md

Abhinav Mohan Bhatia

Systems Engineer | Red Hat | Pune, India

Building at the intersection of operating systems, distributed storage, and cloud-native infrastructure.

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About

Systems engineer at Red Hat, working on storage and Ceph. I enjoy building things from scratch — whether it's an operating system, a robotics pipeline, or a distributed ML platform.


Featured Projects

NovusOSRust AArch64 UEFI

A bare-metal operating system for ARM64 with two components: a monolithic kernel (GICv3 exceptions, 4-level MMU, preemptive scheduler, VirtIO drivers, FAT32 filesystem, TCP/IP stack, userspace via EL1→EL0 transition) and a graphical UEFI desktop with an interactive shell. Boots on QEMU and VMware Fusion.

Monocular-Inertial-SlamC++ OpenCV Arduino

Real-time visual SLAM using ORB-SLAM3 with a Logitech C270 webcam and ICM-20948 IMU connected via Arduino Nano. Includes custom firmware for high-speed IMU streaming, camera-IMU timestamp synchronization, and upstream bug fixes for mutex initialization.

edge-cloud-mlPython FastAPI MQTT Docker

Edge-cloud ML platform for distributed model training and real-time inference on NVIDIA Jetson devices. Server-side model registry with versioning, MQTT-based model distribution to edge agents, and full observability stack with Prometheus and Grafana. Tested on Jetson Nano + Intel laptop over local network.

sos_analyzerPython Streamlit ML

ML-powered analyzer for Linux SOS reports. Detects storage issues across Disk, LVM, RAID, Multipath, and Ceph clusters. Features anomaly detection, failure prediction, trend analysis across historical reports, health scoring, and automated report generation (HTML/PDF). Built for RHEL system administrators.

sample-operatorGo Operator SDK OpenShift

Kubernetes Operator that manages a custom App CRD, reconciling it into Deployments with automatic drift correction, replica scaling, status tracking, and cleanup via OwnerReferences. Built with Operator SDK for OpenShift.

azure-mlops-projectPython Azure Terraform

End-to-end MLOps pipeline on Azure: model training, containerized FastAPI inference service, pushed to ACR, deployed on AKS with Prometheus/Grafana monitoring. Infrastructure provisioned with Terraform. Includes security scanning with Trivy and SonarQube.


Tech Stack

Languages        Rust, C/C++, Python, Go, Bash, Java
Systems          Linux Kernel, UEFI, AArch64, Embedded Linux (ARM)
Storage          Ceph, OpenShift Data Foundation, LVM, RAID, Multipath
Containers       Red Hat OpenShift, Kubernetes, Docker, Podman
Cloud            AWS, Microsoft Azure (AKS, ACR, Key Vault)
ML / Data        PyTorch, TensorFlow, OpenCV, FastAPI, Streamlit
Observability    Prometheus, Grafana, Azure Monitor
Infra-as-Code    Terraform, Operator SDK, Helm
Security         Burp Suite, Nessus, Wireshark, OWASP, Trivy

Certifications

Certification Issuer
Certified Ethical Hacker (CEH) EC-Council
Red Hat Certified System Administrator (RHCSA) Red Hat
Red Hat Certified Specialist in Containers (EX188) Red Hat

I build things that run close to hardware and scale across clusters.

Pinned Loading

  1. azure-mlops-project azure-mlops-project Public

    Python

  2. sample-apps-for-robotics-platforms sample-apps-for-robotics-platforms Public

    Forked from quic/sample-apps-for-robotics-platforms

    C

  3. sample-operator sample-operator Public

    Go

  4. sos_analyzer sos_analyzer Public

    Advanced ML-powered analyzer for Linux SOS reports. Detects storage issues, predicts failures, tracks trends, and generates professional reports. Supports RHEL storage (Disk/LVM/RAID) and Ceph clus…

    Python

  5. edge-cloud-ml edge-cloud-ml Public

    Edge-cloud ML platform for distributed model training and real-time inference on NVIDIA Jetson devices. Built with FastAPI, MQTT, Docker, Prometheus, and Grafana

    Python