Senior AI Engineer

November 4, 2026

Hybrid

Senior

Amsterdam, Netherlands

Indeed

Description

A Senior AI Engineer role focused on building production-ready AI and data products in a hybrid setup. The position covers the full lifecycle from new feature development to infrastructure and data engineering, including LLM applications, RAG pipelines and ML model services. The work includes evaluation frameworks, MLOps on cloud platforms and cross-functional collaboration with product, engineering and science stakeholders.

Responsibilities

Design, develop and maintain production AI solutions, including LLM-based applications, retrieval-augmented generation pipelines, agentic workflows and ML model services
Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic
Define and automate evaluation frameworks, benchmarks and guardrails that measure accuracy, safety, latency and cost, and monitor models once they are in production
Build and optimize data pipelines for training, fine-tuning, embedding and inference, making sure data is high quality, traceable and handled in line with privacy requirements
Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms
Work with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions that meet customer needs
Keep up with the fast-moving AI ecosystem, evaluate new models, tools and techniques, and share what you learn with the wider engineering community

Requirements

Bachelor's degree in Computer Science, Engineering, AI/ML or a related field, or equivalent professional experience
Strong proficiency in one or more high-level programming languages such as C++, Java, Python, or similar languages
Hands-on experience with LLMs and their ecosystem: prompt engineering, RAG, embeddings and vector databases, tool use and agent frameworks, and fine-tuning is preferred
Solid understanding of software architecture, API design, design patterns and best practices for maintainable, scalable systems
Experience with cloud service providers such as Azure, AWS, GCP, containerization with Docker and Kubernetes, and CI/CD tools
Knowledge of version control systems, preferably Git
Excellent problem-solving and communication skills, with the ability to work effectively in a cross-functional team
Experience with ML frameworks and libraries such as PyTorch, TensorFlow or Hugging Face is preferred
Familiarity with MLOps practices and tools such as MLflow, experiment tracking and model monitoring is preferred
Experience with geospatial, mapping or location data is a plus, but not required