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Gaurav Kumar

Lead Cloud Engineer | Rust, Kubernetes, Confidential Computing

B.Tech, Mechanical Engineering (2019) | IIT Kanpur

About Me

I am a passionate software engineer with experience building secure and scalable backend systems, specializing in Rust, Kubernetes, and confidential computing. At Fortanix and Citrix, I've led development across cloud-native services, confidential data pipelines, and AI-integrated systems. I'm deeply focused on designing resilient microservices and solving hard security problems in cloud infrastructure.

Skills & Technologies

Rust
6+ years
Go
2+ years
C++
3+ years
Python
2+ years
Kubernetes
6+ years
Docker
6+ years
Helm
5+ years
Azure
5+ years
AWS
4+ years
GCP
2+ years
Prompt Engineering
2+ years
RAG Systems
3+ years
Vector Databases
2+ years
Qdrant
2+ years
Redis
2+ years
Confidential Computing
3+ years
Grafana
2+ years
Splunk
2+ years
Machine Learning
2+ years

Featured Projects

QA AI – Test Case Generation

Advanced test case generation system using LLM integration with Selenium automation. Streamlines quality assurance processes through intelligent test scenario creation.

View on GitHub

Rust MCP Server

Model Context Protocol (MCP) server implementation in Rust, enabling seamless integration between AI models and development tools with high performance and memory safety.

View on GitHub

Machine Learning Algorithms

Comprehensive collection of machine learning algorithms implemented from scratch, covering supervised, unsupervised learning, and neural networks with practical examples.

View on GitHub

Kubernetes Secret Plugin

Custom Kubernetes plugin for encrypted etcd storage via Data Security Manager (DSM). Enhances cluster security through hardware-backed encryption.

DNS Resolver

Simple and efficient DNS resolver built in Rust that performs both forward domain name resolution and reverse IP lookups with clean, straightforward implementation.

View on GitHub

Confidential RAG Pipeline

Secure LLM integration pipeline with hardware attestation ensuring data confidentiality throughout the retrieval-augmented generation process.

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