Description

Do you have a machine learning model that needs to run reliably in production? Or are you looking to build a scalable, secure ML platform in the cloud?

I help companies design, productionize, and scale machine learning systems — from model development to fully automated MLOps pipelines in AWS or Azure.

With 8+ years of experience in Machine Learning and MLOps across energy, healthcare, and software domains, I specialize in:

Designing scalable ML architectures (AWS, Azure, Kubernetes)

Building MLOps platforms (CI/CD, Infrastructure as Code, model lifecycle management)

Deploying models as APIs (FastAPI) or batch/real-time inference pipelines

Containerization & orchestration (Docker, Kubernetes, AKS, SageMaker)

Model monitoring, retraining pipelines, and governance

LLM PoCs and applied AI use cases

Background in Computer Vision, NLP, OCR optimization, and document AI

What differentiates me?

I combine deep ML expertise with strong cloud and platform engineering skills. I don’t just build models — I make them production-ready, scalable, compliant, and maintainable. I understand both the data science side and the DevOps/cloud side, which allows me to bridge teams effectively.

Typical projects I work on:

Designing end-to-end MLOps pipelines

Migrating ML workloads to AWS/Azure

Setting up training & retraining infrastructure

Building real-time inference systems

If you’re looking for someone who can turn your ML ideas into robust production systems — let’s connect.

Industry field of expertise

Languages

English
Fluent
Dutch
Conversational
Greek
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Alliander
MLOps Cloud Engineer
ENERGY AND UTILITIES
April 2025 - Today (1 year and 2 months)
Alliander, Arnhem, GE, Netherlands
• ● Platform Engineering & Architecture ○ Contributed to design and implementation core components of a scalable ML platform using AWS-native services and Infrastructure as Code (CDK). ○ Built reusable infrastructure constructs and deployment pipelines to standardize model lifecycle management. ○ Contributed to platform architecture decisions focused on security, compliance, scalability, and cost efficiency.
• ● Model Training & Deployment ○ Designed and implemented batch inference pipelines using AWS Step Functions, SageMaker, and Lambda for large-scale ML predictions. ○ Developed real-time inference solutions with SageMaker endpoints and event-driven AWS architectures
• ● Data & Backend Engineering ○ Designed and implemented DynamoDB schemas for platform services, ensuring scalability and performance. ○ Built backend services in TypeScript and Go to support platform orchestration and automation. ○ Developed a CLI tool in Go to improve developer experience and streamline ML workflows.
• ● Observability & Operations ○ Implemented operational monitoring and alerting using CloudWatch. ○ IImproved platform reliability through logging, metrics, and failure handling strategies. ○ Contributed to production-readiness practices including versioning, rollback strategies, and automated deployments.
Typescript AI/ML AWS Python
Human Total Care B.V.,
Machine Learning Engineer
February 2022 - April 2025 (3 years and 2 months)
Utrecht, Netherlands
• ● Productionizing ML Models: ○ Designing the model serving architecture: Creating a scalable and efficient architecture for deploying ML models. Tech used: FastAPI, AzureDevops
○ Dockerization: Packaging ML models into Docker containers, ensuring they can run consistently across different environments. Tech used: Docker
○ Deployment to Kubernetes: Orchestrating the deployment and scaling of ML models using Kubernetes, a container orchestration platform. Tech used: Kubernetes, AzureDevOps, AKS
○ Post-production tasks: Handling tasks related to model maintenance, functional and operational monitoring, and troubleshooting to ensure the models remain effective and reliable. Tech used: Kibana, Evidently, FastAPI, Kubernetes
• ● Architectural Design and Implementation of MLOps Principles
○ Model training and retraining infrastructure: Utilizing kubernetes based infrastructure for training and retraining ML models, allowing for scalability and flexibility as well as data allowing data scientists to prototype, showcase ML models easily. Tech used: Airflow, MLFlow, Postgres, Kubernetes, JupyterHub, Feast, MinIO, Helm, AzureDevOps, AKS.
○ Serving and monitoring: Setting up monitoring systems to track model performance and health in production. Tech used: Evidently, FastAPI, Kubernetes, AzureDevOps, AKS
○ Model retraining: Implementing processes and pipelines for periodic model retraining to keep them up-to-date with new data.
• ● Development of Custom Packages ○ Preprocessing and feature transformation: Creating custom data preprocessing and feature transformation packages tailored to the specific needs of healthcare data. These packages may involve data cleaning, feature engineering, model training/testing, monitoring.
Onior Group B.V.,
Machine Learning Engineer
January 2018 - February 2022 (4 years and 1 month)
The Hague, Netherlands
• ● Applying Machine Learning/ Deep Learning techniques on image and text data.
• ● Training models for image classification, object recognition, clustering, reverse image search, text classification, named entity recognition.
• ● Deploying ML models into production environments.
• ● In general, responsible for almost every step in Machine Learning Engineering, from data preparation, selection of algorithms, training models, testing to Docker containerisation.
• ● Read and replicate scientific papers based on their relevance to encountered challenges.