We are looking for a highly skilled Senior Python / AI Workflow Engineer to help build the next generation of ecological AI infrastructure. You’ll join a team transitioning from scientific research to production systems, working on a modular AI workflow for acoustic detection — a critical component that supports biodiversity protection and regulatory compliance across Dutch municipalities.
You will take a research-grade AI workflow (from a scientific article) and turn it into clean, maintainable, modular Python code that can scale, run reliably in Docker, and integrate into future MLOps pipelines. This role is ideal for a senior engineer who thrives at the intersection of research and production engineering, enjoys structuring complex workflows, and wants to contribute to meaningful environmental impact.
Client — a Netherlands-based organization advancing AI-powered environmental monitoring systems. Their solutions combine machine learning, signal processing, and scalable data pipelines to help local governments and conservationists detect biodiversity changes, ensure regulatory compliance, and protect natural ecosystems.
Requirements: * 5–8+ years of professional experience with Python (production-level, modular codebases) * Strong background in scientific computing (NumPy) and deep learning frameworks (PyTorch or TensorFlow) * Proven ability to translate research code or academic workflows into production-ready systems * Experience with data preprocessing, feature extraction, and model training/evaluation pipelines * Knowledge of data versioning approaches (Git-LFS or similar tools) * Expertise with Docker for ML workloads (image building, reproducibility, runtime optimization) * Familiarity with MLOps tools such as MLFlow, Weights & Biases, or similar experiment-tracking systems * Strong understanding of configuration-driven architecture and modular workflow design * Excellent English communication skills (Upper-Intermediate+)
Responsibilities: * Reproduce an AI workflow described in a scientific research paper using clean, production-oriented Python * Implement modular components for preprocessing, feature extraction, model training, inference, and evaluation * Develop modular, testable, configuration-driven code prepared for pipeline automation * Prepare all components for MLOps integration (logging, experiment tracking, artifacts, metrics) * Contribute to the transition from prototype to scalable deployment across multiple municipalities
We offer: * Paid vacation (up to 20 working days) * Paid sick leaves (10 working days) * National holidays as paid time off * Flexible working schedule and full remote work format * Direct collaboration with the client’s engineering and AI teams * Engaging, meaningful projects with real ecological impact * Opportunity to work with cutting-edge AI technologies and large-scale data systems * Regular online teambuildings and strong team culture
