Professional Summary
Machine learning engineer with 6 years of experience designing production-grade models and data pipelines. Specialises in NLP and computer vision applications that power user-facing features. Combines deep technical expertise with a focus on scalable architecture and continuous delivery.
Skills
- Machine Learning
- Python
- Azure
- AWS
- Pytorch
- Tensorflow
- Deep Learning
- Spark
- NLP
- Docker
- Communication
- Computer Vision
- MLOps
- Data Engineering
- API Design
Experience
Machine Learning Engineer — AdvisoryAI
- Architected real-time recommendation engine serving 10M+ queries/month with sub-200ms latency.
- Implemented CI/CD pipelines for model retraining, reducing deployment cycle from 3 weeks to 3 days.
- Improved NLP entity extraction accuracy by 12% through custom transformer architecture and active learning.
- Designed observability dashboards tracking model drift and inference SLAs across multiple regions.
- Mentored data scientists on productionisation best practices and AWS infrastructure.
Research Engineer — Apple
- Developed computer vision models for ARKit that reduced marker detection latency by 40%.
- Pioneered secure federated learning experiments, maintaining user privacy while improving localisation accuracy.
- Collaborated with design teams to integrate ML features into iOS applications with minimal UX disruption.
- Optimised PyTorch training pipelines on AWS SageMaker, cutting model iteration time from weeks to days.
- Presented findings at internal tech summits, bridging research work with engineering stakeholders.
Education
M.S. Computer Science
Imperial College London
Certifications
- AWS Certified Machine Learning - Specialty
- Microsoft Certified: Azure Data Scientist Associate