Open to opportunities

Fernando Falen

@fernandofalen

Message

AI-augmented full-stack developer and systems engineering student.

Peru

Message

What I'm looking for

I seek roles building AI-driven mobile and backend products where I can apply prompt engineering to accelerate development, contribute to education or mental-health projects, and grow into system architecture and ML responsibilities.

I am an advanced Systems and Computer Engineering student who builds AI-augmented full-stack applications, reducing development time through prompt engineering and pragmatic architecture decisions.

I design and implement mobile apps, scalable backends, and deep learning models—my thesis is a Musical Emotion Detection system (CNN-BiGRU) with a Flask backend, Android client, TFRecord pipelines and PostgreSQL storage, achieving a CCC of 0.63.

I focus on practical, impact-driven products for education and mental health, leverage tools like TensorFlow, Librosa, Firebase and Retrofit, and routinely use LLMs and AI assistants (ChatGPT, Gemini, Cursor AI, Grok, DeepSeek) to accelerate development and optimize solutions.

Experience

Work history, roles, and key accomplishments

AI-Augmented Developer

Self Employed

Designed and delivered AI-augmented full-stack solutions including mobile apps, scalable backends, and deep learning models, reducing development time up to 10x through prompt engineering and AI workflows.

Prompt Engineering Python Flask PostgreSQL Firebase REST APIs

Current

Game Engine Developer

Current

Wave Wave

Jan 2026 - Present (2 months)

Recreated a 2D infinite-runner game engine using Java/LibGDX, implementing procedural obstacle generation, optimized collision detection, and mobile-focused rendering and physics.

Java LibGDX Performance Optimization Algorithm Design Mobile Rendering

Current

Mobile Developer

Current

Visionary AI Gallery

Jan 2026 - Present (2 months)

Built a hybrid mobile gallery with semantic search powered by LLMs (Gemini API) to locate media via natural-language queries, integrating React/TypeScript with native storage and optimized media handling.

React TypeScript Capacitor Gemini API Semantic Search Local Storage Mobile Optimizations Vite

Current

Thesis Developer - Musical Emotion

Current

Santo Toribio de Mogrovejo Catholic University

Jan 2024 - Present (2 years 2 months)

Sole developer of a musical emotion detection DSS mobile app and backend using a hybrid CNN-BiGRU model, achieving a concordance correlation coefficient of 0.63 for valence/arousal prediction and enabling real-time analysis for music therapy.

Deep Learning CNN BiGRU TensorFlow Flask TFRecords PostgreSQL Firebase

Education

Degrees, certifications, and relevant coursework

Santo Toribio de Mogrovejo Catholic University

Bachelor of Engineering, Systems and Computer Engineering

Activities and societies: Thesis: Musical Emotion Detection DSS System with Deep Learning; completion of Thesis Seminar I; projects integrating mobile apps, backend services, and deep learning models.

Currently enrolled in Systems and Computer Engineering, final semesters with thesis work on an AI-driven musical emotion detection system; coursework includes Object-Oriented Programming, Software Design, and Data Mining.