Triumphant Babalola

Built and shipped production LLM-based product features, including structured outputs, Pydantic validation, fallback behavior, and evaluation loops across major AI platforms. Designed evaluation frameworks and CI/CD-integrated regression harnesses, improving benchmark accuracy by 18% and reducing residual risk by 30%.

Architected and shipped a HIPAA-compliant clinical decision support system for patient triage using LangGraph, with PII masking, audit logging, and policy guardrails. Implemented RAG with Pinecone and Weaviate and deployed a FastAPI service to Google Cloud Run serving 10,000+ patients.

Built a Heart Failure Detection clinical AI system with TensorFlow/Keras, achieving 95% accuracy and 0.98 AUC-ROC. Delivered interpretable outputs using Grad-CAM and SHAP and implemented HIPAA/GDPR-compliant encrypted data protocols and audit documentation across a 50+ collaborator team.

Engineered high-throughput FastAPI REST APIs with sub-100ms latency for financial transaction processing using async, event-driven architectures. Built real-time fraud and anomaly detection pipelines with XGBoost and improved performance with quantization that reduced memory footprint by 35%.

Architected a production hybrid recommendation engine combining matrix factorization and content-based filtering, improving personalization metrics by 30%. Built time-series forecasting models with Airflow orchestration and Redis caching, increasing prediction accuracy by 25% and reducing end-to-end latency by 25%.

B.Eng. in Electrical and Electronics Engineering from Landmark University.

AI Security & Governance certification with an expiry noted as November 2027.

Completed the Andrew Ng Deep Learning Specialization.

Completed Stanford CS231n (Convolutional Neural Networks for Visual Recognition).

Completed learning content from fast.ai.