Blossom Williams, MLOps Engineer
C2 (Proficiency) English
Senior (5-10 years)
Canada
Summary
Highly skilled MLOps Engineer with extensive experience in building, deploying, and scaling machine learning models in production environments. Proficient with a range of cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes), adept at implementing CI/CD pipelines (Jenkins, GitHub Actions) for reduced deployment time by 40%, and well-versed in MLOps/DevOps integration for efficient ML model lifecycle management. Holds a strong foundation in computer science with an M.Sc. degree and multiple certifications including AWS Machine Learning Specialty. Proven track record with project achievements like developing an ETL pipeline for real-time analytics and achieving a 20% reduction in transaction fraud through a real-time fraud detection system.
Main Skills
AWS
CI/CD 3 yr.
Kubernetes
Docker
Python 5 yr.
AI & Machine Learning
Programming Languages
Java Frameworks
Scala Frameworks
Data Analysis and Visualization Technologies
Databases & Management Systems / ORM
Cloud Platforms, Services & Computing
Amazon Web Services
Deployment, CI/CD & Administration
Virtualization, Containers and Orchestration
Version Control
Message/Queue/Task Brokers
Platforms
Logging and Monitoring
Work Experience
MLOps Engineer, End-to-End ML Pipeline with Kubeflow
Duration: Unknown specific duration within December 2020 – Present
Summary: Designed and implemented an end-to-end machine learning pipeline with Kubeflow on Kubernetes, focusing on reproducibility and scalability for high-volume daily predictions.
Responsibilities: Automated data ingestion, preprocessing, model training, and deployment using Kubeflow and MLflow.
Technologies: Kubernetes, Kubeflow, MLflow
MLOps Engineer, Real-Time Fraud Detection System
Duration: Unknown specific duration within December 2020 – Present
Summary: Implemented a real-time fraud detection system using a PyTorch-based model which integrated with Kafka and Spark, achieving a 20% reduction in transaction fraud.
Responsibilities: Deployed the PyTorch fraud detection model and integrated with Kafka and Spark for real-time inference on AWS.
Technologies: PyTorch, Kafka, Spark, AWS
DevOps/Cloud Engineer, Cloud Infrastructure and ML Orchestration
Duration: June 2017 – December 2020
Summary: Containerized ML applications and orchestrated with Kubernetes for enhanced scalability and fault tolerance for big data and ML workloads.
Responsibilities: Built and maintained cloud infrastructure, developed ETL pipelines, implemented monitoring and alerting systems.
Technologies: AWS, Azure, Docker, Kubernetes
Education
- M.Sc. in Computer Science
- Memorial University of Newfoundland
- M.Sc. in Computer Science
- University of Debrecen
- B.S. in Computer Science
- Redeemer’s University
Certification
- AWS Certified Machine Learning – Specialty
- TensorFlow Developer Certificate
- Microsoft Certified: Azure Data Scientist Associate