Blossom Williams — MLOps Engineer

Expertise in DevOps (5.5 yr.), Data Engineer (5.5 yr.).

Last verified on October, 2025

Core Skills

AWS

CI/CD

3 yr.

Kubernetes

Docker

Python

5 yr.

Bio 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.

Technical Skills

Programming Languages	Python
Java Frameworks	Apache Spark
Scala Frameworks	Apache Spark
AI & Machine Learning	Kubeflow
Data Analysis and Visualization Technologies	Apache Airflow, Apache Spark, DVC
Databases & Management Systems / ORM	Apache Spark, AWS DynamoDB, ELK stack (Elasticsearch, Logstash, Kibana), MongoDB
Cloud Platforms, Services & Computing	AWS, Azure ML
Amazon Web Services	AWS Cloudformation, AWS CloudWatch, AWS DynamoDB, AWS LightSail
Deployment, CI/CD & Administration	Ansible, CI/CD, GitLab CI, Helm, Jenkins
Virtualization, Containers and Orchestration	Docker, Kubernetes
Version Control	Github Actions
Message/Queue/Task Brokers	Kafka
Platforms	Microsoft Power Platform
Logging and Monitoring	Prometheus

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

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