Advanced Audio MIR & ML Pipeline for DJ Metadata Export

Looking to hire an experienced Audio DSP / Machine Learning Engineer to build an AI-driven Music Information Retrieval (MIR) engine. The system will extract production-focused audio features from finished tracks and export them directly into Rekordbox-compatible metadata.
​Beyond basic tempo and key, the system must leverage Machine Learning models and DSP to automatically calculate deeper sound engineering metrics—sub-bass pressure, full-spectrum RMS density, transient impact, and dynamic punch—and categorize tracks based on acoustic weight and structural energy.
​Scope of Work:
​Develop an autonomous pipeline/script (Python using Librosa/Essentia/PyTorch, or C++/JUCE) that ingests WAV, AIFF, and MP3 files.
​Automatically extract BPM, musical key, and advanced production metrics (sub-bass vs. kick, RMS density, transients, dynamic punch).
​Detect structural shifts (drops, breakdowns, high-energy sections) and calculate macro/micro energy levels automatically.
​Export analysis results into a fully valid Rekordbox XML structure (or ID3 tags) ready for direct library import.
​Acceptance Criteria:
The developer will run the engine on a sample set of audio files. The system must autonomously calculate all production metrics, assign energy levels, and successfully populate custom Rekordbox fields (e.g., Comments, My Tags) upon XML import without requiring manual training data from the user.
​Deliverables:
Clean, well-documented code with concise setup instructions so the feature extraction set can be extended in future iterations.