Brian – Machine learning, Data Science, Big Data

A product for musical artists and rights holders make their catalogs more "commercially viable" using AI as a tool rather than a replacement.

• Curated and unified large-scale audio datasets, building a hierarchical genre/subgenre classification system using AST embeddings and Librosa features, achieving 90%+ genre and 85%+ subgenre accuracy.
• Architected a 6-stage real-time audio processing pipeline on AWS Lambda, SQS, and Apache Iceberg, supporting thousands of tracks and enabling zero-downtime shadow deployments for multiple ML model versions.
• Fine-tuned Stable Diffusion and Flux models with LoRA adapters for automated album artwork generation, and built a multi-model generative media pipeline producing hundreds of production-ready assets.
• Developed end-to-end Metaflow ML pipelines with shared training/inference libraries, automated class balancing, and cold-start optimization, improving inference speed by 4x and reducing AWS costs by 5x.
• Provisioned scalable AWS infrastructure with Terraform and implemented Datadog observability, CI/CD automation, and monitoring across a microservice-based platform.
• Collaborated closely with company leadership to align AI initiatives with product vision and long-term business goals.

The world’s leading digital cross-device graph. It enables marketers to identify a brand customer or related household across multiple devices, unlocking critical use cases across programmatic targeting, media measurement, attribution, and personalization globally.

• Built a terabyte‑scale cookieless household clustering pipeline using community detection and simulated annealing over hundreds of millions of device signals with GDPR‑compliant data retention, replacing legacy deterministic matching and increasing campaign ROI by 20–30%.
• Trained 256‑dim device embeddings via PyTorch neural networks with GPU optimization and loss function experimentation, productionizing DS research models into a real‑time serving platform on a Ray cluster on Kubernetes hosting 1,000+ models, reducing targeting errors by 25%.
• Led the Data & AI Platform initiative, building a custom DSL compiler adopted by 10+ engineers, enabling data scientists to write pipelines once and deploy to Kubeflow, Vertex AI, or Cloud Composer, with 10+ custom TFX components, a Vertex AI feature store, and MLflow for experiment tracking and model registry.
• Architected hybrid NLP + LLM pipelines integrating 10+ consumer data sources into structured, actionable audience profiles, reducing time‑to‑insight for data enrichment products.
• Led fine‑tuning of transformer and LLM models (BERT, GPT variants) for consumer data extraction, classification, and summarization, increasing signal accuracy and cutting manual data processing effort by 40%.
• Integrated multi‑source consumer signals into production pipelines, implementing embeddings and ranking systems to surface high‑value audience segments in real‑time, accelerating client targeting decisions.
• Developed advanced embeddings and clustering frameworks to identify recurring consumer behavior patterns and emerging audience segments, enabling proactive insights that empowered clients to reach high‑value audiences ahead of competitors.

Data & AI platform solutions for various IBM external clients across diverse industries for ensuring the scalability of their data and machine learning models.

• Designed a Data & AI platform supporting terabyte‑scale data and ML pipelines with MLflow for experiment tracking, CI/CD (Jenkins), and Airflow orchestration on Kubernetes, boosting Data Science team productivity by 2x and reducing infrastructure cost by 40%.
• Built an event‑driven real‑time ML serving platform on Kafka with KNN and Locality Sensitive Hashing, reducing inference latency for optimization used daily by users.
• Productionized anomaly detection models by converting research code from Pandas to PySpark, optimizing SQL queries, and orchestrating training pipelines with Airflow on Kubernetes for daily processing of millions of payment records.
• Developed TensorFlow text classification model achieving 92% accuracy for legal ruling prediction; engineered NLP features using n‑grams and custom scoring metrics, improving model accuracy by 11%.
• Fine‑tuned computer vision models for object detection and image classification, delivering production‑ready solutions for enterprise clients.