Turning caffeine into products.
I'm drawn to the hard parts of computing — the parts where you have to understand the hardware, the protocol, or the math to get it right. I work across systems programming, compiler design, blockchain internals, and security, with a published research background in deep learning.
Udyansh
Software Engineer
India · Remote
Mar 2024 – Jun 2025 · 1 yr 3 mos
Vellore Institute of Technology
Bachelor of Technology – Computer Science
Sep 2022 – Aug 2026
Amity International School, Sec 46 Gurugram
High School Education
Apr 2009 – Apr 2022
IEEE – 2023
Enhancing Deep Learning Performance Through Parallel Processing: A Comprehensive Research Study
2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC)
Explores the multifaceted dimensions of enhancing deep learning performance through parallel processing — covering theoretical underpinnings, parallelization strategies, hardware and software infrastructures, and application-specific impact.
Blockchain & Distributed Systems
Layer-1 architecture, Ethereum & EVM internals, execution layer mechanics, consensus design (PoS), mempool architecture, Merkle trees, state transition systems, P2P networking, transaction propagation, smart contracts (Solidity), cryptographic primitives, high-performance blockchain clients in Rust
Systems Engineering
Systems programming in Rust and C, memory-safe architectures, lock-free & concurrent system design, performance engineering, Linux internals, networking stack optimization, high-performance dataplanes (XDP, AF_XDP, DPDK), zero-copy systems, kernel–userspace boundaries
Compilers & Language Design
Parser & lexer implementation, IR design, static analysis, deterministic execution models, reproducible numerics, language tooling, bytecode & VM design, AOT and JIT compilation strategies
Cybersecurity
Exploit development fundamentals, reverse engineering, secure systems design, network security engineering, deep packet inspection (DPI), protocol analysis (TCP/IP, TLS), adversarial simulation, defensive infrastructure architecture
AI & Machine Learning
ML systems engineering, statistical computing infrastructure, reproducible ML pipelines, security-oriented AI systems, performance-aware model deployment, distributed training systems, applied AI in threat detection & anomaly detection
Languages
Python, Rust, C, C++, Go, Java, JavaScript, TypeScript, Perl, SQL, Bash, MATLAB, Dart, Erlang, Assembly
Frameworks
Next.js, React, Flutter, Bun, Tokio, LLVM, WASM, HTML, CSS
Messaging
NATS, Kafka, Redis, ZeroMQ
Databases
PostgreSQL, ClickHouse, MongoDB, MinIO
Protocols
QUIC, HTTP/3, GraphQL
Infra
Docker, Podman, Kubernetes, Cilium, AWS, Linux, eBPF, nftables, SearxNG, Prometheus, Grafana, OpenTelemetry
Kernel
XDP, netfilter, tc, perf, ftrace, bpftrace, kprobes
AI / ML
PyTorch, TensorFlow, JAX, LangChain, LangGraph, Claude, Codex
Why Deleting Data Is Harder Than Writing It
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