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: AWS, Azure, Cloud
• Location requirements: Hybrid, Amsterdam, Netherlands

About the role

• AI Governance Engineer managing technical governance of generative AI tools across the enterprise. Bridging software engineering, security, and compliance for effective AI integration.

Responsibilities

• Design, implement, and maintain the technical governance framework for all GenAI/LLM tools and integrations.
• Evaluate, approve, onboard, monitor, and retire third-party and open-source AI tools (LLMs, vector databases, agents, orchestration platforms, etc.).
• Build and operate usage monitoring, cost tracking, compliance dashboards, and automated audit trails.
• Create lightweight, developer-friendly processes for tool requests, risk assessments, exceptions, and approvals.
• Partner with Legal, Security, Compliance, Procurement, and Engineering to translate policies into enforceable technical controls.
• Identify and mitigate AI-specific risks: prompt injection, data leakage, model misuse, training-data exposure, etc.
• Develop and deliver training, playbooks, workshops, and documentation that get used.
• Stay ahead of evolving regulations, new attack vectors, and model capabilities.
• Continuously optimize the balance between innovation velocity, risk, cost, and compliance.

Requirements

• Strong software engineering background with deep understanding of development lifecycles, CI/CD, APIs, integrations, and versioning.
• Hands-on knowledge of modern GenAI/ML stack: LLMs, embeddings, vector stores, RAG, model endpoints, agents, and orchestration tools.
• Solid experience with at least one major cloud provider (Azure or AWS) – especially AI/ML services, networking, IAM, and governance features.
• Proven ability to interpret security, privacy, data-classification, and responsible AI policies and turn them into practical technical controls.
• Strong grasp of AI-specific security risks (prompt injection, jailbreaks, data exfiltration, inversion attacks, etc.) and mitigation patterns.
• Experience implementing identity & access governance (RBAC/ABAC, least privilege, audit logging) for AI systems.
• Demonstrated skill in cross-functional collaboration and communicating complex technical and risk concepts to non-technical stakeholders.
• Comfort in building processes, dashboards, and lightweight automation from scratch.