Embedded Systems Engineer • Data Engineer

42 Madrid graduate specializing in low-level systems programming (C/C++) and data pipeline engineering (Python). Completed Common Core in 8 months (6% graduation rate), now pursuing 42 Outer Core - Data Engineering track.

• 🎓 42 Madrid - Common Core completed in 8 months (6% graduation rate)
• 📚 Currently: 42 Outer Core (Data Engineering specialization)
• 💼 Seeking: Embedded Software Engineer (C/C++) OR Data Engineer roles
• 💻 Core Stack: C, C++, Python, SQL, Docker, Git
• 🌍 Location: Madrid, Spain 🇪🇸
• 🔗 Portfolio: jainavas.me

42 Outer Core | NEW - January 2026

Virtual machine implementation for CoreWar with advanced code obfuscation tools.

Tech Stack: C, Assembly, Python, ELF Binary Format

Key Features:

• 🖥️ Virtual Machine: Custom VM architecture for executing champion programs
• 🔐 Code Obfuscator: 100% reversible C source obfuscation tool (Python)
• 📊 Visual Illegibility: Uses confusing character combinations (O/0/l/1/I/_)
• 🛡️ Protection System: Preserves C keywords, stdlib, ELF structures, POSIX API
• 🔄 Deterministic: Reproducible obfuscation with seed parameter
• 📁 Dual Mode: Single-file and directory batch processing
• 🧪 Zero Dependencies: Pure Python 3 standard library

Embedded Relevance: Virtual machine design, low-level memory management, assembly language, binary format understanding, toolchain development.

42 Common Core | Systems Programming

Full-featured Bash-like shell with process management, pipelines, and built-in commands.

Tech Stack: C, Unix System Calls, Readline Library

Key Features:

• 🔄 Process Control: Fork/exec process creation and management
• 📡 Pipelines: Multi-command chaining with pipe operator (|)
• 📁 Redirections: Input (<), output (>), append (>>), heredoc (<<)
• 💲 Variable Expansion: Environment variables and exit status ($?)
• 🛠️ Built-ins: echo, cd, pwd, export, unset, env, exit
• 📊 Signal Handling: CTRL-C, CTRL-D, CTRL-\ (POSIX signals)
• 🧠 Memory Safe: Zero leaks (Valgrind validated)

Embedded Relevance: Process management, IPC mechanisms, signal handling, POSIX compliance (critical for embedded Linux systems).

42 Common Core | Dining Philosophers Problem

Classical concurrency challenge implementing thread-safe resource sharing.

Tech Stack: C, POSIX Threads (pthread), Mutexes

Key Features:

• ⚡ Real-time synchronization: Mutex-based resource locking
• 🎯 Deadlock prevention: Fair resource acquisition strategy
• 📊 Precise timing: Microsecond-accurate state management (<10ms death detection)
• 🛡️ Race condition handling: Thread-safe logging and state updates
• 🔄 Resource management: Efficient fork (resource) allocation

Embedded Relevance: RTOS-style task management, critical section handling, timing constraints, essential for real-time embedded systems.

42 Common Core | Graphics Programming

First-person 3D maze renderer using raycasting (Wolfenstein 3D-style).

Tech Stack: C, MinilibX (X11), Raycasting Algorithm, Linear Algebra

Key Features:

• 🎨 Texture mapping: Per-direction wall textures
• ⚡ Real-time rendering: 60 FPS without GPU acceleration
• 🧮 Mathematical optimization: Vector/matrix transformations
• 🎮 Input handling: Smooth keyboard controls with collision detection
• 🗺️ Config parsing: Custom .cub map format

Embedded Relevance: Real-time constraints, CPU-only graphics, memory efficiency (relevant for resource-constrained embedded displays).

42 Common Core | Sorting Algorithm Challenge

Efficient integer sorting using limited stack operations.

Tech Stack: C, Algorithm Design, Complexity Analysis

Key Features:

• 📊 Optimized sorting: <700 operations for 100 integers, <5500 for 500
• 🧠 Custom algorithm: Hybrid approach (radix-inspired with chunk sorting)
• ⚡ Performance: O(n log n) average complexity
• 🔧 Memory efficient: Minimal heap allocation
• 📈 Edge case handling: Duplicates, overflow detection

Embedded Relevance: Algorithm optimization under resource constraints (critical for microcontroller environments).

42 Outer Core | 161 campus completions

End-to-end ML pipeline for plant disease classification from leaf images.

Tech Stack: Python, TensorFlow/PyTorch, OpenCV, NumPy, Pandas

Key Features:

• 🔬 Dataset analysis: Statistical EDA with visualization
• 🔄 Data augmentation: Rotation, scaling, distortion for robustness
• 🧠 Transfer learning: Pre-trained CNN fine-tuning
• 📈 >90% accuracy: Production-ready validation metrics
• 🖼️ Feature extraction: Image preprocessing pipeline

Data Engineering Relevance: ETL pipeline design, data preprocessing at scale, model deployment workflow.

42 Outer Core | Data Science & Logistic Regression

Hogwarts house classification using logistic regression implemented from scratch.

Tech Stack: Python, NumPy, Pandas, Matplotlib

Key Features:

• 📈 Statistical analysis: Descriptive statistics (mean, std, quartiles) from scratch
• 📊 Data visualization: Histograms, scatter plots, pair plots
• 🧠 Logistic regression: Gradient descent optimization (no sklearn)
• 🎯 Multi-class classification: One-vs-all strategy
• 📉 Feature correlation: Discriminative feature identification

Data Engineering Relevance: Statistical computing, data exploration, ML fundamentals without black-box libraries.

42 Outer Core | Q-Learning Agent

Snake game AI using Q-Learning and experience replay.

Tech Stack: Python, Q-Learning, Pygame

Key Features:

• 🤖 Q-Learning agent: State-action-reward training loop
• 🔄 Experience replay: Learn from successful trajectories
• 📊 State compression: Efficient state representation
• 📈 Hyperparameter tuning: Epsilon decay, learning rate optimization
• 🎮 Visual training: Real-time game visualization during training

Data Engineering Relevance: Training pipeline design, state management, performance metrics tracking.

42 Common Core Final | Success Rate: 7%

Real-time multiplayer Pong with microservices architecture.

Tech Stack: TypeScript, Node.js (Fastify), SQLite, Docker, WebSockets

Key Features:

• 📊 Database design: Relational schema for users, matches, stats
• 📈 Monitoring: Grafana + Prometheus for metrics
• 🔄 Real-time data: WebSocket-based event streaming
• 🏆 Analytics: Leaderboards, statistics aggregation
• 🐳 Infrastructure: Docker Compose orchestration

Data Engineering Relevance: Database design, real-time data streaming, monitoring infrastructure, microservices.

• Concurrency: POSIX Threads, Mutex, Semaphores
• IPC: Pipes, Signals, Shared Memory
• System Calls: Process management, File I/O
• Virtual Machines: Custom VM architecture, bytecode execution
• Memory Management: Manual allocation, leak prevention
• Performance: Real-time constraints, optimization
• Assembly: x86, ARM architectures

• Data Processing: Pandas, NumPy, ETL pipelines
• Machine Learning: TensorFlow, PyTorch, Scikit-learn
• Computer Vision: OpenCV, Image preprocessing
• Databases: SQL (SQLite, PostgreSQL)
• Monitoring: Grafana, Prometheus

• Containerization: Docker, Docker Compose
• Version Control: Git, GitHub workflows
• Build Tools: Make, CMake
• Debugging: GDB, Valgrind, AddressSanitizer
• Scripting: Python, Bash

Explore my full portfolio: github.com/jainavas

Notable mentions:

• Lem-in ⭐ 1 - Graph algorithms & flow optimization (C)
• Gomoku - AI game engine with minimax (C++)
• Pipex - Unix pipe mechanism implementation (C)

I'm actively seeking opportunities as:

• 🔧 Embedded Software Engineer (C/C++) - RTOS, drivers, firmware, virtual machines
• 📊 Data Engineer - ETL pipelines, data infrastructure, ML deployment

💼 LinkedIn
🌐 Portfolio

Last updated: February 2026