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
Azure Cloud Services	Azure ML
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

How to hire with Upstaff

1

Talk to Our Talent Expert

Our journey starts with a 30-min discovery call to explore your project challenges, technical needs and team diversity.

2

Meet Carefully Matched Talents

Within 1-3 days, we’ll share profiles and connect you with the right talents for your project. Schedule a call to meet engineers in person.

3

Validate Your Choice

Bring new talent on board with a trial period to confirm you hire the right one. There are no termination fees or hidden costs.

Why Upstaff

Upstaff is a technology partner with expertise in AI, Web3, Software, and Data. We help businesses gain competitive edge by optimizing existing systems and utilizing modern technology to fuel business growth.

Real-time project team launch

<24h

Interview First Engineers

Upstaff's network enables clients to access specialists within hours & days, streamlining the hiring process to 24-48 hours, start ASAP.

x10

Faster Talent Acquisition

Upstaff's network & platform enables clients to scale up and down blazing fast. Every hire typically is 10x faster comparing to regular recruitement workflow.

Vetted and Trusted Engineers

100%

Security And Vetting-First

AI tools and expert human reviewers in the vetting process is combined with track record & historically collected feedbacks from clients and teammates.

~50h

Save Time For Deep Vetting

In average, we save over 50 hours of client team to interview candidates for each job position. We are fueled by a passion for tech expertise, drawn from our deep understanding of the industry.

Flexible Engagement Models

Custom Engagement Models

Flexible staffing solutions, accommodating both short-term projects and longer-term engagements, full-time & part-time

Unique Talent Ecosystem

Candidate Staffing Platform stores data about past and present candidates, enables fast work and scalability, providing clients with valuable insights into their talent pipeline.

Transparent

$0

No Hidden Costs

Price quoted is the total price to you. No hidden or unexpected cost for for candidate placement.

x1

One Consolidated Invoice

No matter how many engineers you employ, there is only one monthly consolidated invoice.

Ready to hire Blossom Williams
or someone with similar Skills?

Book a call with Blossom Williams

Start Hiring