Distributed Systems
Consistency, coordination, replication, and failure handling in real-world production systems.
Systems Engineer • Distributed Systems & AI
Building reliable systems at the intersection of infrastructure and intelligence.
Systems-focused engineer passionate about distributed systems, infrastructure reliability, and AI — building low-level, production-grade systems and exploring how AI reshapes how we design, debug, and scale them.
Distributed Systems OS & Networking AI for Systems
Systems & Infra
OS internals, networking, observability, and building reliable low-level components in Go / Rust / C++.
AI for Systems
Exploring ML-serving infra, anomaly detection, intelligent debugging, and AI-augmented reliability — e.g. partial-convolution inpainting with PyTorch.
I'm especially interested in AI for systems — using learning and automation to make infrastructure more observable, self-healing, and efficient without sacrificing systems rigor.
Experience
Production reliability, distributed debugging, and systems engineering in enterprise environments.
Full work historySeptember 2025 - Present
Solutions Engineer
guardsix (formerly Logpoint)
- Drove root cause analysis on complex technical issues and resolved the failures behind recurring escalations.
- Managed high-priority customer cases with practical workarounds and minimal service interruption.
- Built deeper hands-on debugging capability across Java and Python production environments.
May 2024 - September 2025
Associate Solutions Engineer
guardsix (formerly Logpoint)
- Resolved customer issues across disk management, networking, database operations, and web servers.
- Identified and fixed software defects that improved service stability and runtime behavior.
- Developed Go, Python, and Bash tooling to monitor disk health, memory usage, and network behavior.
Projects
Systems, networking, and AI — from BitTorrent and campus network design to PyTorch inpainting and Go backends.
MapReduce
Distributed MapReduce framework in Go with a long-lived coordinator, worker daemons, and standalone file server. Jobs are submitted as JSON via HTTP API/CLI; workers and coordinator run on different machines with no shared filesystem — all data moves through the file server.
GoDistributed Systems
Aegis: Distributed KV Store with Raft
Single-threaded distributed key-value store in C with Raft for consensus — focused on correctness, log replication, and fault tolerance.
CDistributed Systems
Image Inpainting with Partial Convolution
Encoder-decoder network with partial convolution that automatically reconstructs missing/damaged image regions, preserving visual quality and integrity — served via FastAPI + SvelteKit on Azure.
SvelteKitAzureFastAPIPyTorch
Resume
One-page overview of systems, infrastructure, and AI experience. Available as PDF and interactive view.
Systems Engineer • Lalitpur, Nepal
Nabin Khanal
Focused on distributed systems, OS & networking, and AI-augmented infrastructure. Currently at guardsix building reliability into production systems.
- Systems programming in Go, Rust, C/C++, Python
- Linux, Docker, distributed debugging
- PyTorch • AI for reliability & ML infra
PDF updated August 2026 • Open PDF
Education
Academic background and certifications.
November 2019 - April 2024
Bachelor in Computer Engineering
Pulchowk Campus, Lalitpur
IOE entrance rank 9 out of 12000 aspirants.
2017 - 2019
+2 Science
Nepal Mega College, Babarmahal, Kathmandu
GPA 3.85, All Nepal second rank.