Data Engineer, AI/ML Specialist, and Python Systems Architect. I build high-throughput PySpark Lakehouse pipelines, low-latency deep learning inference engines, and resilient distributed backends.
A breakdown of production technologies, frameworks, and architecture patterns applied across client and enterprise systems.
Building multi-terabyte Lakehouse ETL/ELT architectures with ACID guarantees, schema evolution, and automated data quality checks.
Training and deploying parameter-efficient NLP/ASR models, optimizing deep learning graphs for sub-100ms low-latency inference.
Engineering high-concurrency asynchronous Python backends, containerized microservices, and distributed streaming queues.
Low-resource African languages ASR and Neural Machine Translation system
A high-performance multilingual Automatic Speech Recognition (ASR) and Neural Machine Translation (NMT) platform designed specifically for under-represented African languages, achieving high transcription accuracy and low-latency inference.
Medallion architecture on Azure Databricks, PySpark, and Delta Lake
An enterprise-grade Lakehouse data pipeline ingesting multi-source transactional and telemetry data into Bronze, Silver, and Gold Delta Lake tables with automated schema enforcement, dbt transformations, and data quality observability.
Sub-50ms financial transaction anomaly scoring with PySpark and Redis
A distributed real-time streaming ML pipeline processing payment streams via Apache Kafka, computing real-time feature aggregations in Redis, and executing XGBoost anomaly classification under strict latency SLAs.
A deep dive into query planning, shuffle optimizations, file compaction strategies, and cost-reduction techniques for multi-terabyte Lakehouse pipelines on Databricks.
How to compress transformer and acoustic models for CPU/GPU serving, avoiding PyTorch GIL contention, and achieving ultra-low inference latency under heavy concurrent loads.