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🚀 Quick Start
AI specialist and music technologist with 6+ years building ML-driven solutions at scale. Currently enhancing SoundCloud's recommendation systems and audio analysis capabilities, bridging technical implementation with user-focused product development. Experienced in deploying machine learning systems that serve 200M+ users, collaborating across engineering and product teams to deliver impactful features that connect creators and listeners through music.
🛠 Tech Stack
languages:
primary: [Python, Go, Scala, SQL]
secondary: [Lua, Bash, TypeScript]
frameworks:
web: [FastAPI, Django, React]
ml: [TensorFlow, PyTorch, HuggingFace]
infrastructure:
orchestration: [Kubernetes, Docker]
workflow: [Airflow]
iac: [Terraform]
ci_cd: [GitHub Actions, Jenkins]
databases:
sql: [PostgreSQL, BigQuery]
nosql: [DynamoDB, Redis, BigTable]
ai_ml:
platforms: [MLflow, Kubeflow, vLLM, TGI]
models: [LLMs, CNNs, Transformers]
techniques: [RAG_Systems, Function_Calling, Tool_Use, Quantization]
agentic: [MCP, ADK, A2A]
platform_dx:
apis: [REST, GraphQL, gRPC]
observability: [Prometheus, Grafana, Custom_Metrics]
experimentation: [A/B_Testing, Feature_Flags, Metrics_Analysis]
💼 Experience
Cross-functional collaboration with Product, Design & Engineering teams to deliver ML-powered features for 200M+ users. Led technical implementation of privacy infrastructure, ML serving systems, and developer tooling initiatives using Python, Go, Scala, and TensorFlow.
- Leading Recommendations experiences team to deliver end-to-end product solutions - from data pipelines and algorithm optimization to feature development, cross-team integrations, and UX improvements for 200M+ users
- Spearheaded Agentic Frameworks integration, internal MCP implementations and innovative personalized recommendation systems
- Developing ADK (Agentic Development Kit) for autonomous agent orchestration and A2A (Agent-to-Agent) communication protocols
- Implemented LLM-powered music recommendation engine with persistent user context database for adaptive preference learning
- Built production RAG systems with hybrid search over 100M+ tracks catalog using semantic embeddings and metadata fusion
- Pioneered metrics-driven development, enhancing technical strategy and driving architectural system design decisions aligned with product vision
- Led cloud migration & infrastructure modernization while revamping and improving recommendations products
Built production-ready music analysis systems for audio segmentation and content detection.
- Music Segmentation: Deep learning models using TensorFlow for chorus detection and structure analysis
- NLP Integration: HuggingFace T5 models for audio transcription and vocal detection
- Scale: Processing pipeline handling 10k+ audio files daily with 95% accuracy
- Infrastructure: End-to-end ML pipeline from audio ingestion to insights delivery
Research and development of deep learning models for AI music composition and audio processing using TensorFlow, PyTorch, and C++.
- Audio compression using VAE, Autoencoder, and spectral analysis techniques
- Auto-dynamics processing with real-time amplitude/frequency modulation
- Transformer-based audio generation models for music composition
- Virtual instrument creation pipeline from sound design to sample generation
- Model optimization achieving 60% processing time reduction while maintaining quality
Computer vision and audio processing solutions for multi-modal AI applications.
- Computer Vision: YOLOv4 implementation for object detection and tracking
- Audio Processing: Custom sound detection models and audio-visual synchronization
- MLOps: Pipeline infrastructure and annotation workflows for ML model training
🎓 Certifications
- Lean Product Management - Itamar Gilad (September 2024)
Deep Learning & AI:
- Deep Learning Specialization - DeepLearning.AI (2020)
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
- Generative Deep Learning with TensorFlow - DeepLearning.AI (2021)
Cloud & Infrastructure:
- Google Cloud Platform Big Data and Machine Learning Fundamentals - Google (2021)
- Google IT Automation Professional Certificate - Google (2021)
- AWS Fundamental Course - AWS (2020)
Specializations: Audio Signal Processing, Music Information Retrieval
🏛️ Education
MSc, AI in Sound and Music | Universitat Pompeu Fabra
⏱️ Sep. 19 – Sep. 20 | 📍 Barcelona
Specialized Master's program at the intersection of artificial intelligence and music technology, covering Machine Learning, Deep Learning, Digital Signal Processing, Music Information Retrieval, and Computational Music Creativity for algorithmic composition.
Civil Engineering | University of Balamand
⏱️ Nov. 11 – Jun. 15 | 📍 Beirut
Bachelor's degree in Civil Engineering with specialized focus on Acoustics, covering structural design principles, materials science, and sound engineering applications in architectural and construction projects.
⚙️ Configuration
Language Support
{
"native": ["English", "French", "Arabic"],
"conversational": ["German", "Spanish"],
"programming": ["Python", "Go", "Scala", "SQL", "Lua"]
}
📄 License
This professional profile is licensed under the "Let's Build Amazing Things Together" license.
