AI Engineer Search; Product Engineering
Job in
1000, Amsterdam, Noord-Holland, Netherlands
Listed on 2026-09-23
Listing for:
TomTom International BV
Full Time
position Listed on 2026-09-23
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, DevOps
Job Description & How to Apply Below
We're looking for a skilled Software Engineer to join our team and help us build a production-ready AI product for Tom Tom. The work is full-stack, covering everything from new feature development to infrastructure and client-side integration.
What you'll do- New Feature Development:
Design, implement, and deploy new features using Python to enhance our core AI functionalities. - Dev Ops and MLOps:
Contribute to the development and maintenance of our CI/CD pipelines and MLOps infrastructure. This includes automating model training, deployment, and monitoring processes to ensure product resilience and scalability. - In-house Evaluation System:
Develop and improve our internal evaluation system to accurately measure model performance, track key metrics, and ensure data integrity. - Integration and Deployment:
Manage the integration of our AI solution into client environments. - AI Model Improvement:
Work with our existing models to enhance their performance and efficiency. This will include tasks like fine-tuning large language models and applying advanced prompt engineering techniques. - R&D Contributions:
Participate in short-term research and development projects to explore new technologies and approaches that can give our product a competitive edge. - Product Hardening:
Take ownership of tasks focused on making the product production-ready, including improving reliability, enhancing performance, and implementing robust error handling and monitoring.
- Python Development:
Building new features and improving existing ones. - Dev Ops and MLOps:
Managing CI/CD pipelines and automating model deployment. - Client Integration:
Working on client-side integration - Evaluation Systems:
Developing in-house systems to monitor and measure model performance. - AI/ML Techniques:
Applying fine-tuning and prompt engineering to enhance model performance. - Production Readiness:
Making the product resilient and ready for production, including performance optimization and robust error handling.
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