MLOps Engineer at BioNTech / InstaDeep
John Euko
AI & MLOps Engineer
Building ML platforms that drive efficiency | Leading teams | Published researcher
About Me
What I Do
Design and scale ML infrastructure at BioNTech/InstaDeep. I build CI/CD pipelines, optimize Kubernetes deployments, and reduce cloud costs while accelerating model delivery.
Background
Master's in Computer Science with AI focus. Published researcher with 2 peer-reviewed papers in bioinformatics and ML.
Expertise
Transforming complex ML workflows into production-ready systems using Kubernetes, Docker, and cloud-native solutions.
Technical Skills
MLOps & Platform
Cloud & Infrastructure
Languages & Frameworks
ML & Data
Education
Master's in Computer Science
Artificial Intelligence
Federal University of Sergipe, Brazil
Scholarship RecipientBachelor's in Computer Science
Artificial Intelligence
University of the People, USA
Publications
CoV-UniBind: A Unified Antibody Binding Database for SARS-CoV-2
Bioinformatics Advances, 2025
BioNTech / InstaDeep
View PublicationStock Market Prediction: Integrating Explainable AI with Conv2D Models
WorldCIST 2024
View PublicationAreas of Expertise
MLOps
Building and optimizing scalable ML pipelines with GitHub Actions for automated testing, deployment, and workflow orchestration.
Cloud Computing
Deploying and managing applications on cloud platforms, implementing containerization with Docker, and orchestrating with Kubernetes.
CI/CD Specialist
Expert in GitHub Actions workflows, automating ML model deployment pipelines, and implementing robust CI/CD practices.
Machine Learning
Developing and deploying ML models with automated training and evaluation pipelines, focusing on reproducibility and scalability.
System Integration
Connecting and optimizing systems across different platforms, implementing efficient data pipelines and automated workflows.
API Development
Creating robust FastAPI endpoints and RESTful services, with automated testing and deployment through GitHub Actions.
Personal Infrastructure
Personal Kubernetes Cluster
A self-managed 7-node Kubernetes cluster running on bare metal, featuring 1 master node and 6 worker nodes. This setup demonstrates hands-on experience with container orchestration and infrastructure management.