YASHWANT SINGH RANA

Software engineer.
Systems & GenAI.

Compiler engineering at IBM. Open-source contributions to Qualcomm ELD. Building with C++, Linux, and retrieval-backed AI.

Yashwant working at his desk
Based in Rishikesh, India
8+ production defectsResolved in IBM compiler development
2 merged fixesQualcomm ELD · C++ / LLVM
6 target configurationsFocused ELD regression coverage
01 / EXPERIENCE

Engineering at IBM.

India Software Labs · IBM Z C/C++ compilers

JUL 2025 — SEP 2026Bengaluru, India

Software Engineer

IBM ISL
  • Resolved 8+ production defects across code generation, register allocation, and compiler optimization.
  • Implemented an MCP service for compiler diagnostics, connecting compiler evidence to AI-assisted developer workflows.
  • Debugged memory and multithreading failures with GDB and Linux tools; automated build and test workflows.
JAN 2025 — JUL 2025Bengaluru, India

Software Engineer Intern

IBM ISL
  • Fixed frontend and backend compiler bugs in parsing, AST processing, and code generation.
  • Built Zowe automation scripts and contributed to an AI-assisted developer platform.
02 / SELECTED WORK

Five projects. Code to explore.

Systems, applied AI, and software engineering.

5 featured projects

MERGED INTO QUALCOMM / ELDPR #2011 MergedGNU option compatibility-O1 -O2 -O3PR #2065 MergedLinker-script crash fixPHDRS / FILEHDR

C++ changes. Reproducers. Regression tests.

SYSTEMS / OPEN SOURCE01 / 05

Qualcomm ELD Contributions

Two merged fixes in Qualcomm's LLVM-based ELF linker, improving GNU option compatibility and preventing linker-script crashes.

  • GNU numeric -O compatibility with independent LTO optimization controls.
  • PHDRS/FILEHDR crash fix; focused regressions across 6 target configurations.
  • C++
  • LLVM
  • Linux
  • lit / FileCheck
Implementation & evidence

Object files + linker script → symbol and section layout → ELF executable

  • Accepted numeric GNU -O compatibility options while keeping LTO optimization controls independent.
  • Guarded ELF/program-header address assignment when a linker script omits those headers from the output layout.
  • Reproduced failures and added regression coverage for x86-64, Hexagon, ARM, AArch64, RV32, and RV64 configurations.

Focused validation covered x86-64, Hexagon, ARM, AArch64, RV32, and RV64 configurations, not the full ELD suite. Additional LTO, AArch64, and documentation changes remain under review.

APPLICATION ARCHITECTURE
  1. 01
    Discover propertiesSearch, filter, create, and edit listings
  2. 02
    Handle requestsPHP / Slim routes + MySQL persistence
  3. 03
    Connect integrationsPayment orders · email · PDF contracts

Full-stack prototype · source available

BACKEND / PROPERTY MARKETPLACE03 / 05

Real Estate Aggregator

A property marketplace prototype with searchable listings, account workflows, payment integration, and PDF contracts.

  • Location, budget, availability, and amenity filters backed by MySQL.
  • Razorpay order/signature handling, email OTP flows, and TCPDF contracts.
  • PHP
  • Slim
  • MySQL
  • JavaScript
  • Razorpay
  • TCPDF
Implementation & evidence

PHP/Slim routes → account and property workflows → MySQL + payment/email/PDF integrations

  • Built listing creation/editing and filters for location, budget, availability, and amenities.
  • Implemented registration, login, email OTP/reset flows, and account views.
  • Added Razorpay order creation/signature checks, stored payment records, and TCPDF contract generation.

The repository documents setup and remaining authorization/payment-consistency work. This is a prototype, without a verified production payment deployment.

MODEL TRAINING PIPELINE
  1. 01
    Prepare review pairsAmazon Fine Food Reviews · custom dataset
  2. 02
    Fine-tune GPT-2PyTorch · AdamW · learning-rate schedule
  3. 03
    Generate a summarySaved checkpoint + review prompt

Data preparation → training → inference

NLP / COURSEWORK EXPERIMENT04 / 05

GPT-2 Review Summarization

Fine-tuned GPT-2 to generate short Amazon review summaries, from data preparation to checkpointed inference.

  • Custom PyTorch datasets and loaders for review–TL;DR–summary sequences.
  • AdamW training, learning-rate scheduling, checkpoint save/load, and generation.
  • Python
  • PyTorch
  • Hugging Face
  • pandas
  • Jupyter
Implementation & evidence

Reviews → preprocessing and split → GPT-2 fine-tuning → checkpoint → summary generation

  • Prepared review–TL;DR–summary sequences with a custom dataset and data loaders.
  • Implemented an AdamW training loop, learning-rate scheduling, checkpoint save/load, and prompt-based generation.
  • Included ROUGE/BLEU evaluation code and documented prompt-leakage and batching issues that need correction.

Coursework experiment. Evaluation corrections are documented: historical ROUGE/BLEU scores are not valid quality measurements because of prompt leakage and batch-scoring issues. No benchmark result is claimed.

C++ Concepts Lab's live code workspace showing an unordered_map symbol-table example in the editor.
Live C++ Concepts demo · code workspace
C++ / INTERACTIVE LEARNING05 / 05

C++ Concepts & Systems Lab

Interactive C++ lessons and a C++17 filesystem lab exploring containers, recursive operations, and concurrency.

  • Step-through simulations, an editable compiler workspace, quizzes, and an optional Gemini tutor.
  • cd / ls / cp / mv / rm; sequential and threaded variants with synchronized output.
  • C++17
  • Linux
  • std::thread
  • React
  • Monaco
Implementation & evidence

Concepts: lesson → simulation → editable code → compiler output. Systems: command parser → sequential/threaded commands → filesystem operations.

  • Created data-driven memory/container simulations that explain concepts step by step.
  • Connected the editor to Wandbox for C++, C, Java, and Python, separating compiler output from program output.
  • Added timed quizzes, a browser-local leaderboard, and sanitized Markdown for assistant responses.
  • Implemented command classes for cd, ls, cp, mv, and rm with recursive directory handling.
  • Compared sequential and threaded executables, coordinating listing output with a mutex.
  • Included workload-generation and timing scripts to explore filesystem and concurrency behavior.

Two complementary implementations: a browser learning app and an educational filesystem interpreter. Visuals are teaching simulations, not execution traces. Compilation and AI features require external services. Timing scripts explore concurrency; no measured speedup is claimed.

03 / BACKGROUND

Depth in systems. Range in AI.

I work across compiler internals, C++ systems, and retrieval-backed applications. I enjoy turning a reproducible failure into a focused fix—and making the result easy to inspect and test.

Education

M.Tech · IIIT Delhi
2023–2025 · CGPA 8.37 / 10

B.Tech · Graphic Era Hill University
2019–2023 · CGPA 8.27 / 10

Systems & tooling

C / C++ · Linux · LLVM · GDB · CMake · Ninja · lit / FileCheck

GenAI & machine learning

RAG · BM25 · MiniLM · RRF · LLM APIs · PyTorch · Hugging Face

Application engineering

Python · FastAPI · TypeScript · React / Next.js · SQL · Docker

04 / CONTACT

Let's build useful software.

Interested in SDE, C++ / Systems, and GenAI opportunities.