Olaoluwa D., AI/ML Engineer | End-to-End ML Solutions | PyTorch, TensorFlow, AWS/Azure Cloud
C1 (Advanced) English
Senior (5-10 years)
United Kingdom
UTC+00:00
Summary
Results-driven AI/ML Engineer who transforms data into actionable business insights. Led development of recommendation systems that increased user engagement by 35% and fraud detection models that reduced financial losses by $2M annually. Expert in building scalable ML infrastructure and cross-functional collaboration, with experience managing full ML lifecycle from research to production deployment. Combines strong technical expertise in Python, PyTorch, and cloud technologies with business acumen to deliver measurable ROI through AI solutions.
Main Skills
AI/ML
ML
Python
AI & Machine Learning
Programming Languages
Data Analysis and Visualization Technologies
MLOps Infrastructure Development
- Led team of 4 engineers to build company-wide ML platform
- Reduced model deployment time from weeks to hours with automated CI/CD
- Established monitoring and A/B testing framework for 15+ production models
Research & Publications
- Published 3 papers in top-tier AI conferences (NeurIPS, ICML, ICLR)
- Filed 2 patents for novel deep learning architectures
- Presented at 5+ industry conferences and workshops
Open Source Contributions
- Core contributor to popular ML library with 10K+ GitHub stars
- Created educational content viewed by 50K+ developers
- Mentor for Google Summer of Code and major ML bootcamps
Real-Time Fraud Detection System
- Built ML pipeline processing 1M+ transactions/day with 99.7% accuracy
- Reduced false positives by 45% using ensemble methods and feature engineering
- Technologies: Python, XGBoost, Kafka, Redis, AWS Lambda
Computer Vision for Medical Imaging
- Developed CNN model for early-stage cancer detection with 94% sensitivity
- Collaborated with radiologists to create training dataset of 50K+ images
- Technologies: PyTorch, OpenCV, DICOM processing, AWS SageMaker
Multi-language NLP Chatbot
- Created conversational AI supporting 8 languages with 92% intent accuracy
- Implemented BERT-based models with custom fine-tuning for domain-specific queries
- Technologies: Transformers, FastAPI, Docker, Kubernetes, GCP
Recommendation Engine Optimization
- Redesigned e-commerce recommendation system serving 2M+ daily users
- Improved click-through rate by 28% and conversion rate by 18%
- Technologies: Collaborative filtering, Deep Learning, Apache Spark, Elasticsearch