Hybrid Software Engineer – AI

at TomTom

• Job title: Software Engineer – AI
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python
• Location requirements: Hybrid, Amsterdam, Netherlands

About the role

• Software Engineer developing full-stack AI products for TomTom. Building production-ready features and integration systems for AI solutions.

Responsibilities

• New Feature Development: Design, implement, and deploy new features using Python to enhance our core AI functionalities
• DevOps 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

Requirements

• Python Development: Building new features and improving existing ones
• DevOps and MLOps: Managing CI/CD pipelines and automating model deployment
• Client Integration: Working on client-side integration with any customer-facing TomTom product
• 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
• New Feature Development: Design, implement, and deploy new features using Python to enhance our core AI functionalities
• DevOps 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