Description

Looking for an AI/Backend Engineer who can ship production-grade ML and GenAI solutions — not just prototypes?

I help companies turn AI ambitions into working products. From document summarization systems at Rabobank to real-time data pipelines and NLP-powered chatbots, I build the full stack: Python backend, Azure cloud infrastructure, CI/CD, and the API layer that ties it all together.

What I bring:

With 5+ years of hands-on experience and an MSc in AI from Utrecht University, I operate at the intersection of machine learning engineering and backend development — a combination that's hard to find in one person. I don't just train models; I deploy them, monitor them, and make sure they scale.

What sets me apart:

I've worked across the full AI lifecycle — from research (Utrecht University, Shell) to production systems at Rabobank. I understand both the data science and the software engineering side, which means fewer handoffs, faster delivery, and architectures that actually hold up. I've reduced pipeline latency by 80%, improved processing efficiency by 40%, and built end-to-end agentic AI applications with Azure OpenAI.

Typical deliverables:

→ GenAI / LLM-powered applications (RAG, summarization, chatbots)

→ RESTful APIs with FastAPI or Flask, ready for production

→ Azure-native ML pipelines (Azure DevOps, Docker, Kubernetes)

→ Data ingestion & ETL with PySpark, Airflow, Kafka

→ CI/CD setup and infrastructure-as-code with Terraform

Tech stack: Python · FastAPI · Azure AI · Docker · Kubernetes · PostgreSQL · Redis · PyTorch · TensorFlow · PySpark · Azure DevOps

Available for part-time freelance engagements (evenings/weekends, 8–16 hrs/week). Based in the Netherlands, fluent in English (C2, IELTS 8).

Let's talk about what you're building.

Languages

English
Native or bilingual
Dutch
Basic

Workplace preferences

Can work on-site

Utrecht (up to 50km)

Rabobank
Full Stack AI Engineer
February 2025 - Today (1 year and 4 months)
Utrecht, Netherlands
• • Developed and deployed a document summarization application leveraging Azure OpenAI for Generative AI (LLM) capabilities.
• • Engineered robust back-end logic and implemented comprehensive unit tests in Python; utilized Azure Pipelines and Docker for seamless CI/CD deployment.
• • Designed and implemented RESTful APIs to enable scalable integration and interaction with the summarization service.
• • Defined and developed evaluation metrics to measure summarization quality and guide model improvements.
• • Enhanced user interface and experience by building responsive front-end components with TypeScript and React.
• • Designed and implemented a scalable architecture for long-document summarization, increasing processing efficiency by 40%.
• • Collaborated within a cross-functional team of software engineers, data scientists, and product managers; applied Scrum methodology and Azure DevOps to coordinate tasks and deliver project milestones on time.
Python CI/CD Management API REST artificial intelligence Data integration (ETL, ELT and data warehousing)
Seventrees
AI Analyst
August 2024 - February 2025 (6 months)
Haarlem, NH, Netherlands
• • Developed and deployed a scalable chatbot using NLP and Large Language Models (LLMs), achieving optimal performance by integrating Azure AI services.
• • Led the design and optimization of data pipelines using Apache Airflow, reducing latency by 80% and enabling real-time data flow.
• • Improved model deployment and monitoring by setting up a CI/CD pipeline with Azure DevOps, ensuring smooth operational processes.
• • Managed infrastructure using Kubernetes and Docker, ensuring seamless model deployment and scaling on Azure.
• • Worked in a cross-functional team with software engineers, data scientists, and product managers using Scrum and Jira to coordinate tasks and deliver on project milestones.
Freelancer
Freelance Software Engineer
September 2023 - August 2024 (11 months)
Utrecht, Netherlands
• • Built and deployed machine learning models using TensorFlow, PyTorch, Scikit-learn.
• • Engineered and automated data ingestion pipelines using PySpark and Azure Data Factory, ensuring data readiness for real-time analytics.
• • Utilized MLflow to track experiment results and model performance, reducing the model iteration cycle by 20%.